Accurate anomaly recognition in video surveillance is critical to ensuring public safety, infrastructure protection, and intelligent security operations. However, existing methods often fail to generalize across diverse and dynamic environments due to their limited capacity to model complex spatio-temporal patterns. In this paper, we propose a novel Graph-Based Temporal Attention Memory Network (G-TAMNet) that significantly advances video anomaly detection by capturing intricate temporal dependencies and spatial relationships. By integrating temporal self-attention into a graph convolutional network (GCN) framework, our model enhances the learning of salient and contextually relevant features that traditional approaches tend to overlook. Furthermore, to enable deployment in resource-constrained settings, such as IoTbased surveillance systems, we incorporate model quantization strategies that reduce computational overhead without compromising detection accuracy. Extensive evaluations of three widely used benchmarks, UCFCrime, LAD-2000, and RWF-2000 the effectiveness of the proposed G-TAMNet model, achieving accuracy, precision, recall, and F1-scores of 54.3% / 61.1% / 59.01% / 61.1%,79.3% / 69.1% / 70.0% / 69.1%,and 94.0% / 94.0% / 94.2% / 94.1%, respectively. These results reflect consistent performance gains of 3.3%, 9.9%, and 0.74% in accuracy over existing state-ofthe-art methods on the corresponding datasets. Such improvements underscore the robustness, scalability, and practical viability of GTAMNet for real-time anomaly detection in resource-constrained IoT surveillance systems. In addition, its reliable generalization across diverse environments highlights the potential of the model to advance intelligent security analytics and transform real-world applications in the domain of information forensics.

The past few years have witnessed the rapid development of contrastive graph clustering (CGC). Although a series of achievements have been made, there still remain two challenging problems in the literature. First, previous works typically generate different views via some pre-defined graph augmentation strategies, but inappropriate augmentations may alter the latent semantics of the original data. Second, they often overlook the discriminative unsupervised information when constructing positive and negative sample pairs, resulting in compromised clustering performance. Third, some of them are restricted to only static neighborhood connections for contrastive learning, which neglect the dynamical structural relationship via robust neighboring graph learning. To cope with these issues, this paper proposes a Structure-preserving Contrastive Graph Clustering approach with Dual-channel Label Alignment (SCGC-DLA). In terms of the high-and-low frequency issues, the low-pass and hybrid graph filters are designed for generating two views of reliable augmentations, which can supply rich and complementary information to each other. Further, we construct a structure-preserving matrix, which is derived from the edge betweenness centrality (EBC) perspective design and allows us to efficiently capture the topological relationships among different embedding representations. Under the guidance of the non-dominated sorting theory, the clustering distribution information of dual-channel is used to construct high-confidence pseudo labels. Especially, the generated high-confidence pseudo labels are aligned with latent semantic labels. Finally, the overall network is guided by a self-supervised learning scheme and therefore the final clustering could be obtained. Substantial results on five benchmarks prove the robustness and effectiveness of our approach compared to several state-of-the-arts.

The advancement of sequencing technologies has generated an unprecedented volume of single-cell multi-omics data, providing new opportunities for biological discovery and medical research. However, due to the high heterogeneity across different omics types, effective integration of single-cell multi-omics data remains a critical challenge. Existing methods generally ignore the graph structure information among cells or resort to additional knowledge to construct the cell graphs, leading to suboptimal performance and potentially limited practical utility. In this study, we propose a novel Graph-embedded Deep Generative Clustering model (GeDGC) for single-cell multi-omics data integration. Specifically, GeDGC simultaneously learns the shared latent representations and cluster factors across multiple omics by leveraging Gaussian mixture models. Moreover, we impose the graph embedding constraint on both the latent representations and the cluster assignments to ensure the preservation of intrinsic local data structure among cells. As a result, our model captures complex correlations across omics and obtains informative shared latent embeddings for downstream tasks. Extensive experimental results with seventeen competing methods on ten datasets confirm the superiority of GeDGC in single-cell multi-omics data integration.

Cross-domain recommendation aims to leverage knowledge from multiple domains to mitigate issues of data sparsity and cold–start problems. While traditional cross-domain settings often involve semantic domains (e.g., movies versus books), recent research has expanded this notion to include structure-level domains that reflect different types of interaction graphs. To this end, we proposeSuper–Item Interaction with Contrastive Learning for Structure-level Cross-domain Recommendation(SI${}^{2}$CL), a framework designed to explore item-level structural associations and transfer knowledge across graph-based domains. Specifically, SI${}^{2}$CL integrates a user–item interaction graph and an item–item graph—each capturing explicit and implicit signals, respectively—while addressing the challenge of noisy or sparse connections via contrastive denoising. Super–item interaction further facilitates knowledge transfer by modeling shared preferences of highly connected items and clusters. By reconstructing an item–item graph and aligning it with user feedback through structure-aware contrastive learning, our approach uncovers latent item relationships and improves recommendation robustness. Extensive experiments validate the effectiveness of SI${}^{2}$CL in enhancing both accuracy and diversity in sparse or cold–start recommendation scenarios.

Deep clustering has achieved remarkable progress due to the strong representation learning capabilities of deep neural networks. However, many popular deep clustering algorithms based on contrastive or non-contrastive strategies focus solely on instance-level or cluster-level representation learning, lacking the ability to explore cross-level information necessary for effective clustering. Moreover, these algorithms often overlook the semantic neighborhood information around instances or clusters, which may lead to suboptimal performance. To address these issues, we propose a novel and efficient end-to-end deep clustering framework termed Mutual Neighborhood-Enhanced Representation Learning for Clustering (MNC), which integrates Neighborhood-Enhanced Prototype-level Contrastive Learning (NPCL) with an Aligning with Consistency Samples (ACS) strategy. In particular, the network architecture comprises four main modules, namely, the feature extraction module, the consistency samples search module, the class-level contrastive module, and the instance-level non-contrastive module. Notably, we extend the conventional non-contrastive instance-level representation learning by incorporating operations on deeper neighborhood structures, leveraging mutual neighborhood relationships from dual views. Furthermore, we perform contrastive learning at the cluster-level based on the compact neighborhood structure in a spherical feature space, which encourages intra-cluster compactness and inter-cluster separability. Extensive experiments on five challenging image benchmarks demonstrate the superiority of our approach over the state-of-the-art. In particular, our approach achieves an ACC of 0.342 (0.804) on the Tiny-ImageNet (STL-10) dataset, which surpasses the second-best method by 7.9% and 4.5%, respectively. The code is available at https://github.com/SandLYJ.

Unfair recommendations stem from user-sensitive attributes and information transmission biases. Graph-structured data can provide more balanced information for fair recommendations by capturing multidimensional user–item interactions. However, graph-based fair recommendation still faces some challenges: Traditional graphs rely on static edge-connected topology, struggling to dynamically update many-to-many relationships, which impairs the long-term fairness modeling; Most existing graph mining algorithms overlook individual differences arising from filtered sensitive information, thereby exacerbating the fairness-accuracy tradeoff; Hypergraph neural networks’ propagation relies on structural density, while sparse connections reduce it, leading to inaccurate representations in sparse regions and uneven diffusion. To address these issues, we propose a structure-aware fair recommendation approach based on counterfactual dynamic hypergraphs (FairCH). First, we propose a multidimensional user fairness model that captures many-to-many higher-order user–item relationships and their preference-fairness co-evolution via dynamic hypergraphs. Second, sensitive information is filtered through adversarial learning, and counterfactual hyperedges is reconstructed by counterfactual reasoning, compensating for information loss. Finally, a cross-hierarchy structure-aware model is proposed, which extracts counterfactual fairness layers, global preference layers, and shared evolution layers from hypergraphs and integrates them via an inter-layer interactive attention mechanism to enhance information propagation and mitigate structural biases. Experimental results demonstrate that FairCH exhibits superior recommendation performance to the baselines.

Patents are crucial for protecting technological innovations and fostering competitive advancements in industry. Patent prediction, a novel task in the field of patent mining, aims to forecast future technological trends, providing valuable insights for strategic planning and innovation in the industry. However, the complexity of patent data and the diversity of technological fields make effective patent prediction a significant challenge. Existing methods for predicting scientific research trends struggle to effectively model patent structures and capture dependencies between patents, resulting in suboptimal patent trend predictions. In this article, we propose a novel method, patent prediction with prompt learning (P3L), to achieve effective and accurate prediction of future patent developments based on a pretrained language model (PLM). P3L includes a patent similarity path extraction module to extract multiple patent development paths from extensive datasets. Following this, we design a patent prompt learning approach that integrates patent development paths, keywords, and patent similarities into the prompts. To mitigate potential noise introduced by this integration, we introduce an attention mask matrix for prompt denoising. Finally, we introduce three patent datasets with rich structures, and conduct extensive experiments on these datasets as well as a public dataset, demonstrating the superiority of the proposed method. The dataset and code have been made publicly available athttps://github.com/AllminerLab/P3L

Sequential recommendation systems have become essential for personalized services in e-commerce and content platforms. While recent research has extended these systems with multi-modal features, existing approaches face three major challenges. First, they inadequately model fine-grained temporal interval distributions, failing to discriminate between high-frequency short intervals and low-frequency long intervals. Second, uniform fusion in the time domain leads to semantic misalignment across modalities because it ignores their inherent differences in the frequency domain. Third, rigid fusion strategies without self-supervised constraints lead to limited representation quality and semantic drift from pretrained embeddings. To address these issues, we propose Adaptive Temporal Expert Routing with Hierarchical Wavelet Enhancement (ATHWE) framework. ATHWE employs exponential saturation time mapping to generate temporally adaptive embeddings. These embeddings guide a sparse mixture of experts to model multi-scale user behavior dynamics. A hierarchical wavelet decomposition with band-specific gating selectively fuses complementary frequency components across modalities. Furthermore, contrastive learning and cluster-preserving objectives preserve semantic information during multi-modal fusion. Extensive experiments on multiple datasets validate the effectiveness of our framework. Our code is available at https://github.com/lulusiyuyu/ATHWE .

Graph Neural Networks (GNNs) have demonstrated strong representation learning capabilities in recommender systems, particularly under the contrastive learning paradigm, where the construction of positive and negative sample pairs effectively captures latent relations between users and items, thereby significantly enhancing recommendation performance. However, existing graph contrastive learning methods predominantly rely on static augmentation strategies, lacking adaptability to diverse user behaviors and semantic structures. Moreover, effectively integrating external knowledge (e.g., user attributes and item semantics) into the contrastive learning process remains a major challenge. To address these limitations, we propose ProGraph, a graph prompt tuning framework tailored for recommendation tasks. ProGraph introduces adaptive contrastive learning within the graph prompt mechanism, enhanced by knowledge-aware guidance, to improve both the discriminability and semantic generalization of learned representations. Specifically, it employs structured prompts to guide GNNs in learning embeddings across multiple semantic subspaces, while incorporating knowledge-assisted graph views to preserve structural consistency and better handle heterogeneous attributes. Unlike traditional full-parameter optimization, ProGraph enables efficient tuning with a small number of learnable prompt parameters, thus achieving better transferability and modular compatibility. Extensive experiments on three real-world recommendation datasets with rich interaction records and knowledge attributes demonstrate that ProGraph consistently outperforms several representative state-of-the-art baselines in top-K recommendation performance.

Data clustering aims to partition the input data entities into several disjoint categories, where similar entities are grouped together while dissimilar ones are pulled apart. In general, the existing data clustering methods merely depend on the attribute information or constructed similarity graph information of the input data entities, and such one-sided cues may result in an incomplete understanding and potentially biased conclusions. How to well consider the inherent heterogeneous knowledge of data while maintaining the local semantics remains a challenging problem. To address this, we propose a novel knowledge-aware clustering (KAC) method, where the attribute information and inherent heterogeneous knowledge of data are jointly considered for better cluster structure recovery. Within this framework, there are four major modules. They are: 1) the target attribute autoencoder to capture a customizable target feature representation; 2) the meta-path aggregated knowledge graph encoder to learn an informative graph feature representation; 3) the dual information maximization to ensure the consistent semantics between two embedding representations; and 4) the self-training clustering to self-supervise the clustering learning. Experiments on several real-world datasets are conducted to validate the superiority of KAC, indicating the significance of explicitly considering the heterogeneous knowledge while maintaining local semantics.

Graph unlearning, which aims to eliminate the influence of specific nodes, edges, or attributes from a trained Graph Neural Network (GNN), is essential in applications where privacy, bias, or data obsolescence is a concern. However, existing graph unlearning techniques often necessitate additional training on the remaining data, leading to significant computational costs, particularly with large-scale graphs. To address these challenges, we propose a two-stage training-free approach, Erase then Rectify (ETR), designed for efficient and scalable graph unlearning while preserving the model utility. Specifically, we first build a theoretical foundation showing that masking parameters critical for unlearned samples enables effective unlearning. Building on this insight, the Erase stage strategically edits model parameters to eliminate the impact of unlearned samples and their propagated influence on intercorrelated nodes. To further ensure the GNN's utility, the Rectify stage devises a gradient approximation method to estimate the model's gradient on the remaining dataset, which is then used to enhance model performance. Overall, ETR achieves graph unlearning without additional training or full training data access, significantly reducing computational overhead and preserving data privacy. Extensive experiments on seven public datasets demonstrate the consistent superiority of ETR in model utility, unlearning efficiency, and unlearning effectiveness, establishing it as a promising solution for real-world graph unlearning challenges.

Large Language Models (LLMs) have demonstrated growing potential in Traditional Chinese Medicine (TCM), particularly in diagnostic assistance and knowledge mining. Yet their direct use for prescription generation remains limited by risks of hallucination and lack of clinical safety mechanisms. To bridge this gap, we propose KDA-TCM, a retrievalaugmented diagnostic agent that grounds prescription generation in verified clinical evidence from electronic medical records and classical TCM texts. KDA-TCM operates through a two-phase framework: offline curation of validated diagnostic rules, and online inference guided by multi-task syndrome prediction, dynamic evidence retrieval, and safety-gated generation enforced by clinical constraint modules. Evaluated in both expert-reviewed and real-world settings, KDATCM significantly outperforms existing LLMs in diagnostic fidelity and prescription validity, while maintaining fairness across demographic groups. Expert evaluations and a silent clinical trial further confirm its clinical readiness and reliability. KDA-TCM represents a step toward trustworthy, evidence-based AI for TCM clinical decision support.

Multimodal emotion recognition in conversation (MERC) aims to accurately capture speakers' emotional trajectories by integrating diverse sources such as text, visual cues, and speech. However, existing MERC methods still struggle with conflicts in cross-modal fusion and over-smoothing in deep graph networks. To address these challenges, we propose the Emotion-aware Graph Convolution-based Multimodal Integration Network (EmoMI), a multi-scale heterogeneous graph contrastive learning framework. EmoMI tackles these issues with two core innovations. First, to mitigate fusion conflicts, it employs a novel alternating aggregation scheme, systematically separating interand intra-modal information. Second, to combat over-smoothing, our multi-scale framework generates distinct local and global views of the dialogue graph. We then introduce prototype-level contrastive learning to align stable class “semantic anchors” across these views, providing a robust supervisory signal and enhancing feature discriminability. EmoMI establishes new state-of-the-art results on the IEMOCAP and MELD benchmarks, outperforming strong graph-based baselines. Our findings demonstrate that this synergistic approach effectively resolves long-standing challenges in MERC, paving the way for more robust conversational emotion analysis models.

Large Language Models (LLMs) have demonstrated powerful performance in sequential recommendation due to their robust language modeling and comprehension capabilities. In such paradigms, the item texts of interaction sequences are formulated as sentences and LLMs are utilized to learn language representations or directly generate target item texts by incorporating instructions. Despite their promise, these methods solely focus on modeling the mapping from sequential texts to target items, neglecting the relationship between the items in an interaction sequence. This results in a failure to learn the transition patterns between items, which reflect the dynamic change in user preferences and are crucial for predicting the next item. To tackle this issue, we propose a novel framework for mapping the sequential item texts to the sequential item IDs, named ST2SI. Specifically, we first introduce multi-query input and item linear projection (ILP) to model the conditional probability distribution of items. Then, we further propose ID alignment to address misalignment between item texts and item IDs by instruction tuning. Finally, we propose efficient ILP tuning to adapt flexibly to different scenarios, requiring only training a linear layer to achieve competitive performance. Extensive experiments on six real-world datasets show our approach outperforms the best baselines by 7.33% in NDCG@10, 4.65% in Recall@10, and 8.42% in MRR.

Feature selection, an essential technique in data mining, is often confined to batch learning or online idealization of data scenarios despite its significance. Existing online feature selection methods have specific assumptions regarding the data stream, such as requiring a fixed feature space with an explicit pattern and complete labeling of samples. Unfortunately, data streams generated in many real scenarios commonly exhibit arbitrarily incomplete feature spaces and scarcity labels, making existing approaches unsuitable for real applications. To fill these gaps, this study proposes a new problem called Online Feature Selection with Varying Features Spaces (OFSVF). OFSVF has a three-fold main idea: 1) it leverages Gaussian Copula to model the incomplete feature correlation in a complete latent space, encoded by continuous variables, 2) it employs a novel tree-ensemble-based approach to select the most informative features on-the-fly, and 3) it develops the underlying geometric structure of instances to establish the relationship between unlabeled and labels. Experimental results are documented to demonstrate the feasibility and effectiveness of our proposed method.

Recent advances in pre-trained language models (PLMs), such as GPT, BERT, and their larger variants, have significantly enhanced the ability to generate synthetic datasets for zero-shot learning, where models can generate data without the need for human annotations. However, existing methods often focus on generating domain-specific datasets, which restricts their generalization capabilities when applied to new, unseen domains. Furthermore, many of these methods rely on a single, static dataset that is either too broad or too narrowly tailored to a specific domain, making them less adaptable to diverse downstream domains. In this paper, we propose a dual prompt data generation framework, called Adaptive Domain-Specific Classification via Zero-shot Dataset Generation (AdaGen), which decouples universal representation learning from domain-specific adaptation. The framework consists of two components: domainagnostic training set generation via a universal prompt, followed by domain-specific validation set generation for weight learning. By combining these two components, our method achieves an optimal trade-off between cross-domain robustness and domainspecific accuracy, allowing a small task-aware model to effectively adapt across diverse domains while preserving generalization. This novel method overcomes the limitations of traditional methods by introducing dynamic feedback, enabling efficient zeroshot learning with improved adaptability. Extensive experiments in seven real-world datasets demonstrate the effectiveness of AdaGen, which outperforms the compared baseline models.

In recent years, Large Language Models (LLMs) have gained increasing attention in personalized recommendation systems due to their strong capabilities to understand contextual and semantic information. However, most existing LLM-based recommendation methods primarily rely on textual features, often neglecting the unique structural information embedded in user and item IDs, which are critical for capturing personalized preferences. To bridge this gap, we propose a novel method called CL 3M - Rec, which enhances the semantic representation of user and item IDs through Contrastive Learning (CL) and optimizes LLM recommendation performance via prompt construction. By effectively integrating structured ID information with textual semantics, our approach enables the LLMs to better model individual user interests and improve personalized prediction. Experiments on four public datasets demonstrate that CL3M-Rec consistently outperforms existing LLM-based and CL-based recommendation approaches, achieving superior predictive accuracy and robust generalization across diverse recommendation scenarios. The code of our method is available at https://github.com/Lewis44zhou/CL-3M-Rec.

Generative recommendation has emerged as a promising paradigm aiming at directly generating the identifiers of the target candidates. Most existing methods attempt to leverage prior knowledge embedded in Pre-trained Language Models (PLMs) to improve the recommendation performance. However, they often fail to accommodate the differences between the general linguistic knowledge of PLMs and the specific needs of recommendation systems. Moreover, they rarely consider the complementary knowledge between the multimodal information of items, which represents the multi-faceted preferences of users. To facilitate efficient recommendation knowledge transfer, we propose a novel approach called Multimodal Quantitative Language for Generative Recommendation (MQL4GRec). Our key idea is to transform items from different domains and modalities into a unified language, which can serve as a bridge for transferring recommendation knowledge. Specifically, we first introduce quantitative translators to convert the text and image content of items from various domains into a new and concise language, known as quantitative language, with all items sharing the same vocabulary. Then, we design a series of quantitative language generation tasks to enrich quantitative language with semantic information and prior knowledge. Finally, we achieve the transfer of recommendation knowledge from different domains and modalities to the recommendation task through pre-training and fine-tuning. We evaluate the effectiveness of MQL4GRec through extensive experiments and comparisons with existing methods, achieving improvements over the baseline by 11.18\%, 14.82\%, and 7.95\% on the NDCG metric across three different datasets, respectively.

The anchor-based multi-view clustering is a prominent focus in massive data analysis. Although it has witnessed considerable progress, two critical limitations can still be observed in recent research. First, most existing clustering frameworks construct one anchor graph independently for each view, which may which may overlook the versatile diversity across multi-view latent distributions. In additional, they typically suffer from the two-stage issue, further restricting their flexibility in complex practical scenarios. By considering of this, we derive a new Multi-view Clustering approach via Flexible Dual-level Fusion (MC-FDF). Unlike the traditional one-anchor-graph-per-view practice, the proposed approach first produces a series of diversified anchor graphs from multiple views. Subsequently, these diversified anchor graphs are further fused from a scale-aware perspective, followed by the rotation of scale alignments to capture the consistent clustering structure across different views. With multiple views extended to the multi-scale multi-view paradigm, two levels of granularity (i.e. diversified anchor graph fusion and multi-scale late fusion) are seamlessly formulated into a mutually beneficial framework, in especially a facilitated optimization algorithm with linear time complexity is also provided. Comprehensive evaluations on heterogeneous benchmark datasets confirm that the proposed approach achieves superior performance and scalability over the state-of-the-art baselines.

Multi-view attributed graph clustering (MAGC) has recently experienced impressive attention in the graph exploration literature. Although several excellent achievements have been made, previous MVAGC approaches merely consider the homogeneous information across different views, easily resulting in the compromised results when faced with the heterogeneous graph scenarios. Further, many of them rely on the static neighborhood connection from original attributed graphs, which ignores the dynamical structural relationship for enhancing cross-view contrastive learning. To deal with these drawbacks, this paper derives a Dual-level Facilitated Multi-view Contrastive Graph Clustering (DF-MCGC) approach. Specifically, we design a hybrid graph filter by considering the homogeneity hidden in individual view. Further, the view-consistent topology invariant matrix is derived to exploit the topological relationship among different embedded representations. This design progressively helps to construct the cluster-wise sample pairs for cross-view contrastive learning. Especially, the overall network is incorporated with a dual-level self-supervised paradigm, among which the self-supervised signals can be efficiently facilitated in a mutually enhanced manner. Experiments on heterogeneous benchmarks have confirmed the advantages of our DF-MCGC approach in comparison with the advanced competitors.

In the digital age, recommendation systems are crucial for enhancing user experiences, with bundle recommendations playing a key role by integrating complementary products. However, existing methods fail to accurately identify user preferences for specific items within bundles, making it difficult to design bundles containing more items of interest to users. Additionally, these methods do not leverage similar preferences among users of the same category, resulting in unstable and incomplete preference expressions. To address these issues, we propose Preference Identification by Interaction Overlap for Bundle Recommendation (PIIO). The data augmentation module analyzes the overlap between bundle-item inclusions and user-item interactions to calculate the interaction probability of non-interacted bundles, selecting the bundle with the highest probability as a positive sample to enrich user-bundle interactions and uncover user preferences for items within bundles. The preference aggregation module utilizes the overlap in user-item interactions to select similar users, aggregates preferences using an autoencoder, and constructs comprehensive preference profiles. The optimization module predicts user-bundle matching scores based on a user interest boundary loss function. The proposed PIIO model is applied to two bundle recommendation datasets, and experiments demonstrate the effectiveness of the PIIO model, surpassing state-of-the-art models.

Graph Neural Networks (GNNs) have demonstrated remarkable proficiency in handling a range of graph analytical tasks across various domains, such as e-commerce and social networks. Despite their versatility, GNNs face significant challenges in transferability, limiting their utility in real-world applications. Existing research in GNN transfer learning overlooks discrepancies in distribution among various graph datasets, facing challenges when transferring across different distributions. How to effectively adopt a well-trained GNN to new graphs with varying feature and structural distributions remains an under-explored problem. Taking inspiration from the success of Low-Rank Adaptation (LoRA) in adapting large language models to various domains, we propose GraphLoRA, an effective and parameter-efficient method for transferring well-trained GNNs to diverse graph domains. Specifically, we first propose a Structure-aware Maximum Mean Discrepancy (SMMD) to align divergent node feature distributions across source and target graphs. Moreover, we introduce low-rank adaptation by injecting a small trainable GNN alongside the pre-trained one, effectively bridging structural distribution gaps while mitigating the catastrophic forgetting. Additionally, a structure-aware regularization objective is proposed to enhance the adaptability of the pre-trained GNN to target graph with scarce supervision labels. Extensive experiments on eight real-world datasets demonstrate the effectiveness of GraphLoRA against fourteen baselines by tuning only 20% of parameters, even across disparate graph domains. The code is available at https://github.com/AllminerLab/GraphLoRA.

Abstract Recommendation system technologies predominantly focus on user-item interaction data, which are mapped into shared vector spaces for digital representation. These representations are then analyzed to uncover the relationships between users and items. As recommendation technologies have seen widespread adoption, a novel challenge has emerged in supply-demand matching contexts: the dual-triangular recommendation problem, involving four key entities, i.e., users with their demands, and suppliers with their offered items, forming a heterogeneous information network. In this work, we introduce the concept of dual-triangular recommendation and formally define this scientific problem. We propose a dual-triangular recommendation algorithm, enhanced by large language models, which utilizes knowledge graph encoder and LLM-augmented encoder to generate embedding representations for the four entities. A multi-task framework is employed to enable the sharing of underlying parameters across multiple recommendation tasks within the dual-triangular context. Through extensive experiments conducted on a real-world technology commercialization platform dataset, patent transfer dataset, and talent recruitment dataset, we demonstrate the effectiveness of our approach, offering a feasible and scalable solution to the dual-triangular recommendation problem.

Federated learning (FL) gained importance in sensitive Internet of Things (IoT) environments by creating a privacy-preserving ecosystem where participants share machine-learning models instead of raw data. However, FL shifts data control away from the server, exposing it to non-independent and identically distributed (non-IID) problems caused by biased clients (IoT devices). This hinders the learning process by increasing execution time and cost. Current solutions alter the FL structure or compromise privacy by offloading clients’ raw data to an external server. To mitigate these limitations, this article proposes a solution to the non-IID problem by introducing an initialization phase, orchestrated by the server, that constructs high-quality initial models. These models can boost FL accuracy and convergence, regardless of whether IoT participants exhibit non-IID properties. Our proposed initialization scheme involves clients training over the same model sequentially, lessening the impact of aggregation, a primary cause of model degradation in federated approaches. Furthermore, a regulator algorithm deployed on the server maintains model integrity and mitigates catastrophic forgetting, enhanced by a client selection process that emphasizes the compatibility of IoT clients to cooperate effectively. Moreover, we devise an optimization scheme based on clustering and genetic algorithms to reduce the selection time while ensuring optimal performance in IoT networks. Experiments on MNIST, KDD, and CIFAR10 data sets show promising results in terms of initial model resiliency against catastrophic forgetting and non-IID settings. Additionally, our findings suggest that our approach can significantly enhance FL training in IoT applications by achieving 40% higher initialization accuracy and a 20% average improvement in end results compared to conventional methods, all while reducing computation time by 80% compared to similar approaches.

With the growing privacy and data contamination concerns in recommendation systems, recommendation unlearning, i.e., unlearning the impact of specific learned data, has garnered more attention. Unfortunately, existing research primarily focuses on the complete unlearning of target data, neglecting the balance between unlearning integrity, practicality, and efficiency. Two major restrictions hinder the widespread application of this unlearning paradigm in practice. First, while prior studies often assume consistent similarity among samples, they overly emphasize the local collaborative relationships between samples and central nodes, leading to an imbalance between local and global collaborative information. Second, while data partition appears to be a default setup, this evidently exacerbates the sparsity of recommendation data, which can have a potentially negative impact on recommendation quality. To fill these gaps, this paper proposes a data partitioning and submodel training strategy, named Partition Distinction with Contrastive Recommendation Unlearning (PDCRU), which aims to balance data partitioning and feature sparsity. The key idea is to extract structural features as global collaborative information for samples and introduce structural feature constraints based on sample similarity during the partitioning process. For submodel training, we leverage contrastive learning to introduce additional high-quality training signals to enhance model embeddings. Extensive experiments validate the feasibility and consistent superiority of our method over existing recommendation unlearning models in learning and unlearning. Specifically, our model achieves a 4.83% improvement in performance and a 4.64x enhancement in unlearning efficiency compared to baseline methods. The code is released at https://github.com/linli0818/PDCRU.

Due to the wide existence of unlabeled graph-structured data (e.g., molecular structures), the graph-level clustering has recently attracted increasing attention, whose goal is to divide the input graphs into several disjoint groups. However, the existing methods habitually focus on learning the graphs embeddings with different graph reguralizations, and seldom refer to the obvious differences in data distributions of distinct graph-level datasets. How to characteristically consider multiple graph-level datasets in a general well-designed model without prior knowledge is still challenging. In view of this, we propose a novel Graph Prompt Clustering (GPC) method. Within this model, there are two main modules, i.e., graph model pretraining as well as prompt and finetuning. In the graph model pretraining module, the graph model is pretrained by a selected source graph-level dataset with mutual information maximization and self-supervised clustering regularization. In the prompt and finetuning module, the network parameters of the pretrained graph model are frozen, and a groups of learnable prompt vectors assigned to each graph-level representation are trained for adapting different target graph-level datasets with various data distributions. Experimental results across six benchmark datasets demonstrate the impressive generalization capability and effectiveness of GPC compared with the state-of-the-art methods.

Multi-view clustering (MVC) is essential for integrating heterogeneous data from multiple sources. However, many existing approaches are hindered by high computational complexity and the separate optimization of similarity and cluster structures. In light of these challenges, this paper presents a novel anchor-based MVC method termed simple one-step multi-view clustering with fast similarity and cluster structure learning (SONIC), which models adaptive anchor learning, multi-view similarity structure learning, and discrete cluster structure learning in a joint framework. In particular, we employ the anchor-based multi-view similarity learning to capture the consensus manifold structure latent in multiple views, thereby constructing a unified bipartite graph with adaptive anchor learning and view weighting. Then we impose a low-rank constraint on the bipartite graph structure to directly reveal the desired number of clusters without additional post-processing. An efficient alternating minimization algorithm is developed to optimize the model, resulting in a computational complexity that scales linearly with the number of samples. Extensive experiments on eight benchmark datasets demonstrate the superior performance of SONIC in both clustering quality and computational efficiency. Code available:https://github.com/huangdonghere/SONIC.

Drug combination therapy with significant advantages is a well-established concept in cancer treatment. Some related efforts have been made with multiple artful deep learning techniques. However, they are usually based on data for drug synergy prediction, ignoring the professional characteristics of data and the systematic knowledge accumulation. Meanwhile, integrating the dispersed professional knowledge and effectively utilizing it in data remains a crucial technical challenge. In this study, we propose KSDDC, a novel model for knowledge-aware synergistic discovery of drug combinations from a large language model (LLM) perspective (i.e., from the continuously learnable and refined large database). Within this framework, three main modules are well-designed, i.e., knowledge-aware drug feature auto-encoding, knowledge-aware cell line feature encoding and drug-drug synergy prediction. Informative embeddings of samples are discovered and combined to make accurate drug synergy prediction. Overall, KSDDC is superior compared with the other shallow machine learning based methods and deep learning based methods on several synergistic prediction benchmarks, where about 19% F1-score improvements over the second best method on DrugComb_1 can be observed. Starting with drug synergy prediction, our studies with knowledge-enabled data mining offer valuable insights and serve as a reference method for future research in this field.

Cross-domain recommendation (CDR) has received attention to solve the cold-start and data sparsity problems. Existing methods mainly focus on the information about overlapping users or items, neglecting to effectively and efficiently utilize the information about nonoverlapping users or items in the source domain. Currently, graph prompt learning is proposed to bridge the gap between the pretrained tasks and downstream tasks, which can fully use the information of the source domain. However, existing graph prompt based CDR methods are rare and solely focus on the user-item interaction graphs without considering extra auxiliary information. Therefore, in this article, we construct knowledge graphs (KGs) as auxiliary information and propose a novel knowledge-aware graph prompt tuning for CDR (KGP-CDR) model. First, the KGs of the source and target domains are constructed, respectively, and a graph encoder is pretrained on the KG of the source domain. In addition, two types of graph prompts are designed: soft graph prompts and personalized graph prompts. These prompts are finetuned in the target domain along with the pretrained graph encoder. Ultimately, the predicted rating can be acquired through the finetuned graph prompts. Experiments on five real-world datasets show that the proposed method performs better than the state-of-the-art methods.

Attributed graph clustering, aiming to discover the underlying graph structure and partition the graph nodes into several disjoint categories, is a basic task in graph data analysis. Although recent efforts over graph contrastive clustering have achieved decent performances, most of them get accustomed to construct the positive neighbor set by the generated pseudo clustering information, directly ignoring the ready-made neighbor nodes and the underlying semantics of edges in a graph. How to well deal with the graph neighbor-specific information to facilitate the performance of graph contrastive clustering is still a challenging problem. Therefore, in this work, we propose a novel Homophily Induced Contrastive Attributed Graph Clustering (HomoCAGC) method, where the power of homophily is exploited in facilitating the performance of contrastive attributed graph clustering, while the pseudo homophily in a graph is also explored and distinguished. Especially, the node feature as reliable information guidance is used to compute the underlying feature-oriented pair-wise node similarity, based on which the positive node pairs in contrastive regularizer are adjusted for better node representation characterization. According to the refined node representations, a triplet self-supervised clustering objective is well-designed to ensure the output embedding is cluster-oriented, and suitable for the downstream clustering task. Extensive experiments on seven benchmarks are conducted to demonstrate the effectiveness of HomoCAGC.

The anchor-based multi-view subspace clustering (AMSC) has turned into a favorable tool for large-scale multi-view clustering. However, there still exist some limitations to the current AMSC approaches. First, they typically recover anchor graph structure in the original linear space, restricting their feasibility for nonlinear scenarios. Second, they usually overlook the potential benefits of jointly capturing the inter-view and intra-view information for enhancing the anchor representation learning. Third, these approaches mostly perform anchor-based subspace learning by a specific matrix norm, neglecting the latent high-order correlation across different views. To overcome these limitations, this article presents an efficient and effective approach termed Large-Scale Tensorized Multi-View Kernel Subspace Clustering (LTKMSC). Different from the existing AMSC approaches, our LTKMSC approach exploits both inter-view and intra-view awareness for anchor-based representation building. Concretely, the low-rank tensor learning is leveraged to capture the high-order correlation (i.e., the inter-view complementary information) among distinct views, upon which the \(l_{1,2}\) norm is imposed to explore the intra-view anchor graph structure in each view. Moreover, the kernel learning technique is leveraged to explore the nonlinear anchor–sample relationships embedded in multiple views. With the unified objective function formulated, an efficient optimization algorithm that enjoys low computational complexity is further designed. Extensive experiments on a variety of multi-view datasets have confirmed the efficiency and effectiveness of our approach when compared with the other competitive approaches.

Electroencephalography (EEG) is a vital non-invasive technique used in neuroscience research and clinical diagnosis. However, EEG data have a complex non-Euclidean structure and are often scarce, making training effective graph neural network (GNN) models difficult. We propose a "pre-train, prompt" framework in graph neural networks for EEG analysis, called GNN-based EEG Prompt Learning (GEPL). The framework first uses unsupervised contrastive learning to pre-train on a large-scale EEG dataset. It then transfers the generic EEG knowledge learned by the model to target EEG datasets through graph prompt learning, thereby enhancing the model's performance with a limited amount of EEG data from the target domain. We tested the framework on five EEG datasets, and the results showed that GEPL outperformed traditional fine-tuning methods in classification accuracy and area under the ROC curve (AUC). GEPL demonstrated improved generalization, robustness, and computational efficiency, thereby significantly reducing the overfitting risks associated with limited EEG data. Moreover, the model provided interpretable results, highlighting relevant brain regions during classification tasks. This research suggests that the "pre-train, prompt" paradigm is well-suited for EEG analysis and offers potential applications in other domains where data are limited.

Clinical staging of liver cancer (CSoLC), an important indicator for evaluating primary liver cancer (PLC), is key in the diagnosis, treatment, and rehabilitation of liver cancer. In China, the current CSoLC adopts the China liver cancer (CNLC) staging, which is usually evaluated by clinicians based on radiology reports. Therefore, inferring clinical information from unstructured radiology reports can provide auxiliary decision support for clinicians. The key to solving the challenging task is to guide the model to pay attention to the staging-related words or sentences, and the following issues may occur: 1) Imbalanced categories: Early- and mid-stage liver cancer symptoms are subtle, resulting in more data in the end-stage. 2) Domain sensitivity of liver cancer data: The liver cancer dataset contains substantial domain knowledge, leading to out-of-vocabulary issues and reduced classification accuracy. 3) Free-text and lengthy report: Radiology reports sparsely describe various lesions using domain-specific terms, making it hard to mine staging-related information. To address these, this article proposes a large language model (LLM)-based Knowledge-aware Attention Network (LKAN) for CSoLC. First, for maintaining semantic consistency, LLM and a rule-based algorithm are integrated to generate more diverse and reasonable data. Second, an unlabeled radiology corpus is pre-trained to introduce domain knowledge for subsequent representation learning. Third, attention is improved by incorporating both global and local features to guide the model's focus on staging-relevant information. Compared with the baseline models, LKAN has achieved the best results with 90.3% Accuracy, 90.0% Macro_F1 score, and 90.0% Macro_Recall.

Clinical staging is crucial for treatment strategies and improving 5-year survival rates in hepatocellular carcinoma (HCC) patients. However, existing methods struggle to distinguish stages with highly similar textual features. Additionally, their lack of interpretability hampers their practical application in medical scenarios. Here, we introduce KnowST, a joint-knowledge network designed to leverage task relevance to explore implicit knowledge for interpretable staging prediction of liver cancer. First, the relevance of auxiliary tasks and the main task is established from two perspectives to guide the model's focus on staging-related implicit knowledge in radiology reports. Stages-to-stages: KnowST learns the inter-stage distinctions between different stages and the similarities within the same stages, using these as important references for staging differentiation. Factors-to-stages: Clinically, staging is determined by multiple tumor factors. These factors can serve as effective clues to assist KnowST in predicting the correct stage, especially in the case of confusing stages. Second, domain-specific word embeddings are introduced to bridge the gap between pre-trained language models and Chinese radiology reports. Lastly, tumor factor prediction enhances the credibility of the deep model in staging prediction, and its visualized results effectively demonstrate the model's interpretability. Overall, KnowST leverages the joint-knowledge from these two perspectives, effectively utilizing implicit information in radiology reports to achieve interpretable clinical staging. Compared to the optimal baselines, KnowST improves AUC by 7.69% and achieves 90.52% accuracy on 573 real-world radiology reports, while also demonstrating superior stage identification and stable performance across various metrics.

Online learning, characterized by its feature space’s adaptability over time, has emerged as a flexible learning paradigm that has attracted widespread attention. However, existing online learning methods often overlook the distributional differences between instances and the presence of label noise in streaming data, thus significantly hindering the effectiveness and robustness of these algorithms. To overcome these challenges, we propose an online confidence learning algorithm for noisy labeled features, which aims to achieve robustness against arbitrary data streams and noisy labels. It employs two new strategies: online confidence inference, which applies the principle of empirical risk minimization to identify inconsistencies in spatial distributions, and geometric structure learning, which utilizes dynamic instance confidence to compute disparities between instances and their labels. Empirical findings demonstrate that our label correction mechanism enhances classification accuracy more effectively across various types of noisy labels (i.e., symmetric, asymmetric, and flipped). Additionally, a case study on image datasets was conducted to illustrate in detail the effectiveness of our OLNLS algorithm. Code is released in https://github.com/Zhuosd/OLNLS .

Effectively and efficiently mining valuable clustering patterns is a challenging problem when handling large-scale data from diverse sources. Existing approaches adopt anchor graph learning or binary representation embedding to reduce computational complexity. Normally, anchor graph learning can not directly obtain the clustering assignment except adopt the post-processing stage, such as graph cut or k-means clustering. The binary representation embedding neglects the structure information in Hamming space. In order to overcome these limitations, this paper proposes a novel, effective, and efficient angular reconstructive discrete embedding method with fusion similarity for a multi-view clustering (AFMC) that can jointly learn the global and local structure preserving binary representation and clustering assignment. Specifically, we propose to use angular reconstructive error minimization to maintain the global similarity correlation of binary representations of heterogeneous features in a common Hamming space. Moreover, we design a multi-view discrete ridge regression with fusion similarity term to handle the out-of-sample problem and preserve the local manifold structure. In addition, we propose an efficient optimization algorithm with linear computational complexity to solve the non-convex and non-smooth objective function. The experimental results demonstrate that AFMC outperforms several state-of-the-art large-scale multi-view clustering methods.

Patent data generally includes information from different perspectives or different types, and its heterogeneous attributes can be greatly beneficial to data clustering analysis. However, the existing patent analysis method always focus on the patent text cues, and such a strategy merely depends on the feature information to capture the data characteristics, failing to multi-type informative patent representation. Therefore, in this paper, to model the underlying structure/relationships of patent data, we employ the knowledge graph to depict the heterogeneous attributes of patent, and propose a novel Knowledge Graph-based Patent Clustering (KGPC) method, where the relationship reconstruction in knowledge graph as well as clustering-oriented representation refinement for patent clustering are jointly considered. With this model, there are three components, i.e., entity representation refinement, relationship reconstruction and self-supervised entity clustering. Given a patent knowledge graph as input, the entity representation refinement can be mutually boosted by the relationship reconstruction and self-supervised clustering objective, thereby leading to a balanced clustering-oriented output. Extensive experiments on several real-world patent knowledge graph datasets validate the effectiveness of KGPC while compared with the state-of-the-art.

Multiview clustering (MVC) aims to integrate multiple related but different views of data to achieve more accurate clustering performance. Contrastive learning has found many applications in MVC due to its successful performance in unsupervised visual representation learning. However, existing MVC methods based on contrastive learning overlook the potential of high similarity nearest neighbors as positive pairs. In addition, these methods do not capture the multilevel (i.e., cluster, instance, and prototype levels) representational structure that naturally exists in multiview datasets. These limitations could further hinder the structural compactness of learned multiview representations. To address these issues, we propose a novel end-to-end deep MVC method called multilevel contrastive MVC (MCMC) with dual self-supervised learning (DSL). Specifically, we first treat the nearest neighbors of an object from the latent subspace as the positive pairs for multiview contrastive loss, which improves the compactness of the representation at the instance level. Second, we perform multilevel contrastive learning (MCL) on clusters, instances, and prototypes to capture the multilevel representational structure underlying the multiview data in the latent space. In addition, we learn consistent cluster assignments for MVC by adopting a DSL method to associate different level structural representations. The evaluation experiment showed that MCMC can achieve intracluster compactness, intercluster separability, and higher accuracy (ACC) in clustering performance. Our code is available at https://github.com/bianjt-morning/MCMC.

Ensemble clustering aims to combine different base clusterings into a better clustering than that of the individual one. In general, a co-association matrix depicting the pairwise affinity between different data samples is constructed by average fusion or weighted fusion of the connective matrices from multiple base clusterings. Despite the significant success, the existing works fail to capture the global structure information from multiple noisy connective matrices. Meanwhile, the locality property of the resulting representation matrix could not be explicitly preserved. In this article, we propose a novel contrastive ensemble clustering (CEC) method. Specifically, a consensus mapping model is designed for the discovery of the latent representation from the noisy observations with distinct confidences. Furthermore, a contrastive regularizer is dexterously formulated to refine the latent representation while preserving its locality property. Extensive experiments conducted on several benchmark datasets demonstrate the superiority of the proposed CEC method. To the best of our knowledge, it is the first time to explore the potential of latent representation learning and contrastive components for the ensemble clustering task.

Multiview attribute graph clustering aims to cluster nodes into disjoint categories by taking advantage of the multiview topological structures and the node attribute values. However, the existing works fail to explicitly discover the inherent relationships in multiview topological graph matrices while considering different properties between the graphs. Besides, they cannot well handle the sparse structure of some graphs in the learning procedure of graph embeddings. Therefore, in this article, we propose a novel contrastive multiview attribute graph clustering (CMAGC) with adaptive encoders method. Within this framework, the adaptive encoders concerning different properties of distinct topological graphs are chosen to integrate multiview attribute graph information by checking whether there exists high-order neighbor information or not. Meanwhile, the number of layers of the GCN encoders is selected according to the prior knowledge related to the characteristics of different topological graphs. In particular, the feature-level and cluster-level contrastive learning are conducted on the multiview soft assignment representations, where the union of the first-order neighbors from the corresponding graph pairs is regarded as the positive pairs for data augmentation and the sparse neighbor information problem in some graphs can be well dealt with. To the best of our knowledge, it is the first time to explicitly deal with the inherent relationships from the interview and intraview perspectives. Extensive experiments are conducted on several datasets to verify the superiority of the proposed CMAGC method compared with the state-of-the-art methods.

Over the past few years, cross-domain recommendation has gained great attention to resolve the cold-start issue. Many existing cross-domain recommendation methods model a preference bridge between the source and target domains to transfer preferences by the overlapping users. However, when there are insufficient cross-domain users available to bridge the two domains, it will negatively impact the recommender system's accuracy (ACC) and performance. Therefore, in this article, we propose to create a link between the source and the target domains by leveraging knowledge graph (KG) as the auxiliary information, and propose a novel knowledge-reinforced cross-domain recommendation (KR-CDR) method. First of all, we construct a new cross-domain KG (CDKG) by using the KGs that represent the source and target domains, respectively. Additionally, we employ reinforcement learning (RL) with meta learning on CDKG to discover meta-paths between the source and target domains. With these meta-paths, we obtain meta-path aggregated embedding vectors for cold-start users. Ultimately, the predicted rating can be acquired from the user meta-path aggregated embedding vector and item embedding vector. Experiments carried out on five real-world datasets show that the proposed method performs better than the state-of-the-art methods.

Multiview attributed graph clustering is an important approach to partition multiview data based on the attribute characteristics and adjacent matrices from different views. Some attempts have been made in using graph neural network (GNN), which have achieved promising clustering performance. Despite this, few of them pay attention to the inherent specific information embedded in multiple views. Meanwhile, they are incapable of recovering the latent high-level representation from the low-level ones, greatly limiting the downstream clustering performance. To fill these gaps, a novel dual information enhanced multiview attributed graph clustering (DIAGC) method is proposed in this article. Specifically, the proposed method introduces the specific information reconstruction (SIR) module to disentangle the explorations of the consensus and specific information from multiple views, which enables graph convolutional network (GCN) to capture the more essential low-level representations. Besides, the contrastive learning (CL) module maximizes the agreement between the latent high-level representation and low-level ones and enables the high-level representation to satisfy the desired clustering structure with the help of the self-supervised clustering (SC) module. Extensive experiments on several real-world benchmarks demonstrate the effectiveness of the proposed DIAGC method compared with the state-of-the-art baselines.

Knowledge distillation (KD), as an effective compression technology, is used to reduce the resource consumption of graph neural networks (GNNs) and facilitate their deployment on resource-constrained devices. Numerous studies exist on GNN distillation, and however, the impacts of knowledge complexity and differences in learning behavior between teachers and students on distillation efficiency remain underexplored. We propose a KD method for fine-grained learning behavior (FLB), comprising two main components: feature knowledge decoupling (FKD) and teacher learning behavior guidance (TLBG). Specifically, FKD decouples the intermediate-layer features of the student network into two types: teacher-related features (TRFs) and downstream features (DFs), enhancing knowledge comprehension and learning efficiency by guiding the student to simultaneously focus on these features. TLBG maps the teacher model's learning behaviors to provide reliable guidance for correcting deviations in student learning. Extensive experiments across eight datasets and 12 baseline frameworks demonstrate that FLB significantly enhances the performance and robustness of student GNNs within the original framework.

The integrity of training data, even when annotated by experts, is far from guaranteed, especially for non-independent and identically distributed (non-IID) datasets comprising both in- and out-of-distribution samples. In an ideal scenario, the majority of samples would be in-distribution, while samples that deviate semantically would be identified as out-of-distribution and excluded during the annotation process. However, experts may erroneously classify these out-of-distribution samples as in-distribution, assigning them labels that are inherently unreliable. This mixture of unreliable labels and varied data types makes the task of learning robust neural networks notably challenging. We observe that both in- and out-of-distribution samples can almost invariably be ruled out from belonging to certain classes, aside from those corresponding to unreliable ground-truth labels. This opens the possibility of utilizing reliable complementary labels that indicate the classes to which a sample does not belong. Guided by this insight, we introduce a novel approach, termed gray learning (GL), which leverages both ground-truth and complementary labels. Crucially, GL adaptively adjusts the loss weights for these two label types based on prediction confidence levels. By grounding our approach in statistical learning theory, we derive bounds for the generalization error, demonstrating that GL achieves tight constraints even in non-IID settings. Extensive experimental evaluations reveal that our method significantly outperforms alternative approaches grounded in robust statistics.

Reciprocal recommender systems (RRS) have been widely used in online platforms such as online dating and recruitment. They can simultaneously fulfill the needs of both parties involved in the recommendation process. Due to the inherent nature of the task, interaction data is relatively sparse compared to other recommendation tasks. Existing works mainly address this issue through content-based recommendation methods. However, these methods often implicitly model textual information from a unified perspective, making it challenging to capture the distinct intentions held by each party, which further leads to limited performance and the lack of interpretability. In this paper, we propose a Knowledge-Aware Explainable Reciprocal Recommender System (KAERR), which models metapaths between two parties independently, considering their respective perspectives and requirements. Various metapaths are fused using an attention-based mechanism, where the attention weights unveil dual-perspective preferences and provide recommendation explanations for both parties. Extensive experiments on two real-world datasets from diverse scenarios demonstrate that the proposed model outperforms state-of-the-art baselines, while also delivering compelling reasons for recommendations to both parties.

Metaverse has gained rapid interest from the research community due to its various promising features. These features include proactive learning and self-sustainability. These features enable wireless systems with analysis prior to deployment, efficient resource management with strict latency, and operation with the least possible intervention from the operators. Therefore, deploying the metaverse for wireless systems will offer many benefits. However, metaverse signaling itself requires novel design. Therefore, in this work, a new framework using network virtualization for the metaverse is proposed. Such virtualization will provide more flexibility in terms of management by using the shared physical network resources to support different services. The proposed framework takes into account three key stakeholders, namely, the network operators, a metaverse operator, and the end-users. Resources will be purchased by a metaverse operator from network operators and allocated to users in the proposed framework. To carry out this interaction, a cost function is defined that considers both communication resources and computing resources. By optimizing communication resource management, computing resource management, association, communication resources cost, and computing resource management costs, the cost function is minimized. To achieve this, we propose a strategy based on matching theory and convex optimization. In order to demonstrate the effectiveness of the proposed scheme, numerical results are provided in the final section.

Due to the emergency of multi-view graph data, considerable attention is focused on the multi-view graph clustering. Although great efforts have been made in developing the multi-view graph clustering methods, most of them implicitly follow the homophily assumption, where the connected nodes with edges tend to be in the same category. As a matter of fact, such an ideal assumption is hard to be satisfied in the real-world graph data, and there are some heterogeneous edges connecting dissimilar nodes in graph. How to well consider the homophily and refine the noisy/heterogeneous edges in multi-view graph clustering still remains an under-explored challenge. Therefore, in this paper, we propose a Homophily-enhanced Adaptive Graph Refinement for Multi-view Graph Clustering (HomoMGC) method, where an adaptive graph refinement strategy is seamlessly designed. Specifically, a feature-oriented graph is constructed based on the shared feature, and an integrated graph is computed by averagely fusing all the input adjacent graphs. Then, the feature-oriented graph and integrated graph are stacked into a graph tensor with a low-rank tensor constraint, where a refined affinity probability matrix can be adaptively recovered from the integrated graph by considering multiple graph information as well as the semantics features. Extensive experiments on several benchmark datasets demonstrate the superiority of HomoMGC compared with the state-of-the-art graph clustering methods. For the code reproducibility, the source code of HomoMGC is public available at https://github.com/ManshengChen/Code-for-HomoMGc-master.

In the evolving landscape of sequential recommendation systems, the application of Large Language Models (LLMs) is increasingly prominent. However, current attempts typically utilize general-purpose LLMs, which present a mismatch in capability and a large semantic gap relative to the specialized needs of recommendation tasks. To tackle these issues, we introduce RecCoder, an innovative model that reformulates sequential recommendation as a code completion task. This approach leverages the superior reasoning capability of code LLMs as a backbone, aligning well with the requirements of recommendation systems. To bridge the semantic gap, RecCoder creates extra tokens for each item and employs item content to initialize token embeddings. Furthermore, we have developed a suite of Semantic Adaptation Fine-tuning tasks, tailored to enhance the model's acquisition of both content and collaborative semantic information, thus aligning the model's intrinsic capabilities with the unique demands of recommendation tasks. Through extensive testing on three public datasets, RecCoder has shown remarkable improvements over existing models in terms of recommendation accuracy and efficiency. This success highlights the substantial yet previously underexplored potential of code LLMs in improving recommendation accuracy and efficiency, suggesting a promising new direction for future research in this area. The implementation code is accessible at https://github.com/AllminerLab/Code-for-RecCoder-master.

In next basket recommendation, baskets are usually formed through a large number of user interactions with items in the early stage. In general, the existing methods for next basket recommendation primarily focus on historical purchase behavior of users, assuming that user purchase interests are static, and overlook the dynamic and diverse changes in user purchase interests. In order to fully capture dynamic user interests and provide users with more diverse recommendations, we propose our method, Dynamic Heterogeneous Graph Prompt (DHGP), for next basket recommendation. By constructing a dynamic heterogeneous graph, we can adequately consider the influence of various interactive behaviors on the user's baskets at different times. Furthermore, we introduce a periodic dynamic heterogeneous prompt strategy to capture the interest directions between baskets from different users and provide users with more diverse interest directions. Extensive experimental validation on six real world datasets demonstrates that our method shows strong applicability across datasets under various conditions and outperforms several state-of-the-art recommendation methods. To the best of our knowledge, DHGP is the first next basket recommendation method that effectively combines dynamic and heterogeneous information. The implementation code is accessible at https://github.com/AllminerLab.

With the explosive growth of information, recommendation systems have emerged to alleviate the problem of information overload. In order to improve the performance of recommendation systems, many existing methods introduce Large Language Models to extract textual information from description text. However, Large Language Models are trained on large-scale generic textual data and may face a semantic gap for downstream recommendation tasks. To address the above issues, we propose Contrastive Learning for Adapting Language Model to Sequential Recommendation (CLA-Rec). In CLA-Rec, we first extract text embeddings from description text using Large Language Models and align the text embeddings learned by Large Language Models with the collaborative information through contrastive learning to obtain high-quality item representations. Through semantic alignment, we bridge the semantic gap between Large Language Models and the recommendation task. To map textual information and collaborative information into user representations, we utilize a Transformer model to learn user representations and capture user preferences by combining the semantically aligned item representations. Extensive experiments on three public datasets demonstrate that our method outperforms state-of-the-art approaches on multiple evaluation metrics, illustrating the effectiveness of the CLA-Rec model in adapting Large Language Models to recommendation tasks.

Next-basket recommendation (NBR) infers a set of items that a user will interact with in the next basket. Existing methods often struggle with the data sparsity problem, particularly when the number of baskets is significantly large due to diverse user behaviors. Cross-domain recommendation (CDR) can effectively alleviate this problem in NBR by transferring knowledge across different domains. Nevertheless, these methods often rely on the similarities of overlapping users, which leads to the negative transfer problem and ignores the overlapping items that are general in real-world scenarios like chain stores. In this paper, we provide a clear symbolic definition of cross-store recommendation (CSR) and distinguish it from CDR. We also propose a novel CSNBR model for cross-store next-basket recommendation task. To fully model the transferable collaborative information between two stores, we learn the embeddings of users, baskets, and items by two intra-store bipartite graphs, and use an inter-store unified bipartite graph to transfer the previously learned knowledge. Furthermore, to alleviate the negative transfer problem, we propose to reconstruct the inter-store unified bipartite graph by utilizing user embeddings obtained from the transfer layer and the disentanglement layer. We also employ two sequence encoders to model the historical sequential information at basket-level and item-level. Extensive experiments conducted on real-world datasets demonstrate the effectiveness of the CSNBR model.

Contrastive clustering has recently been an emerging topic in deep unsupervised learning. Nevertheless, the previous works mostly adopt the stochastic data augmentations, which easily leads to the semantic drift problem by limited transformations. Moreover, these approaches ignore the data distribution information when generating the positive and negative pair-wise samples. In light of this, this paper proposes a simple yet effective unsupervised clustering network termed Confidence-oriented Contrastive Graph Clustering (CoCGC). Particularly, we design an end-to-end network paradigm with un-shared weights, among which a hybrid graph filter is utilized to generate two views of reliable augmentations. Guided by the non-dominated sorting theory, we further construct a confidence-oriented sample set from the latent data distribution perspective. By considering the local density and cluster distribution of the embedding representations, the discriminative sample pairs can be derived from the confidence-oriented sets in a two-view contrastive manner. Finally, a cross-view neighbor contrastive loss is devised for better exploiting the self-supervised network signals. Extensive experimental results on five benchmark datasets demonstrate the effectiveness of our method against the existing state-of-the-art deep graph clustering methods.

Traffic prediction is a complex task. In the traditional forecasting methodologies, there are limitations regarding generalization, as specifically observed in the pretraining approaches. This is particularly evident in models involving temporal and spatial attributes, where finetuning often encounters numerous challenges. With the emergence of graph prompt learning, a new finetuning paradigm has gained increasing application. In the context of the traffic prediction methods involving spatio-temporal attributes, we endeavor to enhance its performance in the downstream tasks through graph pretraining and prompt-based finetuning. In particular, we propose a traffic prediction model based on graph prompt learning. This model deeply mines the hierarchical information of traffic data and its temporal attributes, by employing the method of graph pretraining and prompt learning to enhance the model’s generalizability and accuracy. Extensive experiments conducted on two real-world traffic prediction datasets confirm the effectiveness of the proposed method.

As the Metaverse develops, it is becoming more crucial to prioritize the safety of users, especially regarding the potential risks, such as users experiencing dizziness or making incorrect movements that may lead to falls. With more virtual environments becoming increasingly available and immersive, detecting and preventing falls within the Metaverse is required. Given the constrained resources of wearable sensors, precise fall prediction models are critical to efficiently analyzing data gathered by these devices. Traditional fall detection systems require centralizing data collection, which raises privacy concerns over the collected data. Resource-aware Split Federated Learning (RSFL) enables collaboration among multiple devices within the Metaverse to train a fall detection model, all while preserving individual data privacy. The approach also leverages parallelism in Federated Learning (FL) and Split Learning (SL) by decomposing training tasks between clients and servers. Moreover, we devise an efficient client selection mechanism to ensure timely training and model convergence performance. We implemented our architecture and assessed its performance using a sensory dataset. The evaluation results with the baseline demonstrate our architecture's superiority in terms of convergence time. Our approach mitigates data heterogeneity and privacy concerns, creating secure and efficient fall detection systems for the Metaverse.

Abstract Recently, with graph neural networks (GNNs) becoming a powerful technique for graph representation, many excellent GNN-based models have been proposed for processing heterogeneous graphs, which are termed Heterogeneous graph neural networks (HGNNs). However, existing HGNNs tend to aggregate information from either direct neighbors or those connected by short metapaths, thereby neglecting the higher-order information and global feature similarity information in heterogeneous graphs. In this paper, we propose a Multi-View Heterogeneous graph neural network (MV-HGNN) to aggregate these information. Firstly, two auxiliary views, specifically a global feature similarity view and a graph diffusion view, are generated from the original heterogeneous graph. Secondly, MV-HGNN performs two message-passing strategies to get the representation of different views. Subsequently, a transformer-based aggregator is used to get the semantic information. Subsequently, the representations of the three views are fused into a final composite representation. We evaluate our method on the node classification task over three commonly used heterogeneous graph datasets, and the results demonstrate that our proposed MV-HGNN significantly outperforms state-of-the-art baselines.

This paper focuses on two limitations to previous multi-view clustering approaches. First, they frequently suffer from quadratic or cubic computational complexity, which restricts their feasibility for large-scale datasets. Second, they often rely on a single graph on each view, yet lack the ability to jointly explore many versatile graph structures for enhanced multi-view information exploration. In light of this, this paper presents a new Scalable Multi-view Clustering via Many Bipartite graphs (SMCMB) approach, which is capable of jointly learning and fusing many bipartite graphs from multiple views while maintaining high efficiency for very large-scale datasets. Different from the one-anchor-set-per-view paradigm, we first produce multiple diversified anchor sets on each view and thus obtain many anchor sets on multiple views, based on which the anchor-based subspace representation learning is enforced and many bipartite graphs are simultaneously learned. Then these bipartite graphs are efficiently partitioned to produce the base clusterings, which are further re-formulated into a unified bipartite graph for the final clustering. Note that SMCMB has almost linear time and space complexity. Extensive experiments on twenty general-scale and large-scale multi-view datasets confirm its superiority in scalability and robustness over the state-of-the-art.

Due to the capability of encoding auxiliary information for alleviating the data sparsity issue, knowledge graph (KG) has gained an increasing amount of attention in recent years. With auxiliary knowledge about items, the KG-based recommender systems have achieved better performance compared with the existing methods. However, the effectiveness of the KG-based methods highly depends on the quality of the KG. Unfortunately, KGs are usually with the problem of incompleteness and sparseness. Besides, the existing KG-based methods could not discriminate the importance of different factors that users consider when making decisions, which may degrade the interpretability of the methods. In this article, we propose a recommendation model named community enhanced knowledge graph for recommendation (CEKGR). By adding entities and relations, the KG is enriched with more semantic information, which would help mine users’ preference for better recommendation. With weights of each path, the interpretability of the recommendation can be improved. To validate the effectiveness of the proposed method, we conduct experiments on three public datasets. Experiment results have shown the improvement compared with other state-of-the-art methods. Besides, case study has illustrated the interpretability of the proposed recommendation model.

Although recommender systems have achieved considerable success, sometimes it is difficult to convince users due to the failure to explain the recommendation results. For this reason, explainable recommender systems have drawn a lot of attention in recent years. Among explainable recommendation models, the meta-path-based model plays a significant role because it can reason over the path connecting a user–item pair to achieve explainability. However, it is difficult for the meta-path-based model to achieve such a common explanation in collaborative filtering as “a user similar to you has purchased item$A$” because there is no such meta-path. In this article, we contribute a new model named collaborative meta-path modeling for explainable recommendation (COMPER). It models the similarity of user pairs and item pairs through rating information and constructs collaborative meta-paths for explainability. In addition, we design an attention mechanism to aggregate different paths connecting the target user and the target item. Moreover, the information of the subgraph composed of all paths connecting the target user and the target item is integrated for rating prediction. Extensive experiments on five real-world datasets demonstrate that COMPER achieves good performance in a variety of scenarios, achieving improvements over several baselines.

Deep contrastive clustering has recently gained significant attention due to its advantageous ability to leverage the contrastive learning paradigm for joint representation learning and clustering. However, previous deep contrastive clustering approaches mostly focus on instance discrimination or cluster discrimination, which often overlook the rich semantic information latent in the vastintermediatelevels of granularity between instances and clusters. Moreover, they are typically prone to utilizing relationships only within the same level of granularity, e.g., instance-instance relationships and cluster-cluster relationships, but frequently neglect the interactions between different granularity-levels that are ubiquitous in real-world scenarios. To tackle these issues, this paper presents a novel end-to-end deep contrastive clustering approach termed Deep Clustering with Hybrid-Grained Contrastive and Discriminative Learning (DCHL). Particularly, the instance-level contrastive learning and cluster-level contrastive learning are first formulated, where the cluster-level contrastive learning is further split into fine-grained and coarse-grained branches. To capture the global dependencies, the cluster-level contrastiveness is explored on the coarse-grained cluster branch. Meanwhile, to capture the hybrid-grained relationships, the dual-level instance-group discrimination learning is enforced between the instance branch and the fine-grained cluster branch, where the self instance-group discrimination and the cross instance-group discrimination are simultaneously optimized for enhancing the deep clustering performance. Experimental results on five challenging image datasets confirm the superiority of DCHL over state-of-the-art. Code available: https://github.com/dengxiaozhi/DCHL.

Multiview clustering plays an important part in unsupervised learning. Although the existing methods have shown promising clustering performances, most of them assume that the data is completely coupled between different views, which is unfortunately not always ensured in real-world applications. The clustering performance of these methods drops dramatically when handling the uncoupled data. The main reason is that: 1) cross-view correlation of uncoupled data is unclear, which limits the existing multiview clustering methods to explore the complementary information between views and 2) features from different views are uncoupled with each other, which may mislead the multiview clustering methods to partition data into wrong clusters. To address these limitations, we propose a tensor approach for uncoupled multiview clustering (T-UMC) in this article. Instead of pairwise correlation, T-UMC chooses a most reliable view by view-specific silhouette coefficient (VSSC) at first, and then couples the self-representation matrix of each view with it by pairwise cross-view coupling learning. After that, by integrating recoupled self-representation matrices into a third-order tensor, the high-order correlations of all views are explored with tensor singular value decomposition (t-SVD)-based tensor nuclear norm (TNN). And the view-specific local structures of each individual view are also preserved with the local structure learning scheme with manifold learning. Besides, the physical meaning of view-specific coupling matrix is also discussed in this article. Extensive experiments on six commonly used benchmark datasets have demonstrated the superiority of the proposed method compared with the state-of-the-art multiview clustering methods.

Word polysemy poses a formidable challenge in the semantic similarity task, especially for complex Chinese semantic information. However, most existing methods tend to emphasize information expansion, often overlooking the fact that the added information may be either irrelevant or only weakly correlated. In view of this, we propose a novel approach that fuses knowledge enhancement and context filtering to achieve self-selective semantic expansion. This approach, termed mutual-scenario-aware multimodal-enhanced representation learning (MuSAM), integrates information across multiple modalities. Specifically, we extend individual words within three modalities, and the extended information with weak correlation is filtered and denoised, to get the mutual-scenario extended information. The extended information with strong correlation in each modality will be fused to obtain the multimodal fusion representation vector of the word pair. Experimental evaluations conducted on five datasets underscore the superiority of the MuSAM model over state-of-the-art methods, showcasing a performance improvement ranging from a minimum of 4% to a maximum of 21%. Remarkably, our model is designed in a completely engineering manner, which can be applied to real scenarios directly without manual intervention.

The social relationships among users can be effectively represented using graph structures, which has led to increasing interests in utilizing graph neural networks (GNNs) for social recommendation. However, there are still some inevitable issues in the existing methods: 1) The problem of sparse supervision signals in the GNN-based recommendation models has not been well addressed. 2) The existing social recommendation methods often neglect the guiding effect of the auxiliary behaviors on the target behaviors, where only the single target behavior data are used for model training. 3) In the GNN-based social recommendation algorithms, the dynamics of recommendations are rarely considered. To address these issues, this article proposes a time-aware multibehavior contrastive learning framework. To achieve better-personalized recommendation, we perform representation learning from multiview perspectives, incorporating temporal information and multibehavior interactions into the social recommendation. A time-aware GNN is then designed to model the dynamic dependency relationships between users and items, by which the dynamics of recommendations can be enhanced. Meanwhile, we propose a multibehavior contrastive learning framework to rationalize the use of multibehavioral data and address the problem of sparse supervision signals. Extensive experiments on three real-world datasets further validate the superiority of our method, where the maximum improvement can reach 6.14% in terms of NDCG@5.

In recent years, the recognition of human emotions based on electrocardiogram (ECG) signals has been considered a novel area of study among researchers. Despite the challenge of extracting latent emotion information from ECG signals, existing methods are able to recognize emotions by calculating the heart rate variability (HRV) features. However, such local features have drawbacks, as they do not provide a comprehensive description of ECG signals, leading to suboptimal recognition performance. For the first time, we propose a new strategy to extract hidden emotional information from the global ECG trajectory for emotion recognition. Specifically, a period of ECG signals is decomposed into sub-signals of different frequency bands through ensemble empirical mode decomposition (EEMD), and a series of multi-sequence trajectory graphs is constructed by orthogonally combining these sub-signals to extract latent emotional information. Additionally, to better utilize these graph features, a network has been designed that includes self-supervised graph representation learning and ensemble learning for classification. This approach surpasses recent notable works, achieving outstanding results, with an accuracy of 95.08% in arousal and 95.90% in valence detection. Additionally, this global feature is compared and discussed in relation to HRV features, with the intention of providing inspiration for subsequent research.

Most existing large-scale multiview clustering algorithms attempt to capture data distribution in multiple views by selecting view-wise anchor representations beforehand with$k$-means, or by direct matrix factorization on the original observations. Despite impressive performance, few of them have paid attention to the semantic correlations between anchor bases and cluster centroids, or even the underlying relations between clusters and data samples. In view of this, we propose aConceptFactorization basedMultiviewClustering for Large-scale Data (CFMC) method with nearly linear complexity. The anchor bases learning, coefficient expression with clear semantic cues and partitioning are integrated together in this unified model. Meanwhile, explicit connections among multiview data, anchor bases and clusters are modeled via coefficient representations with semantic meanings. A four-step alternate minimizing algorithm is designed to handle the optimization problem, which is proved to have linear time complexityw.r.t.the sample size. Extensive experiments conducted on several challenging large-scale datasets confirm the superiority of the method compared with the state-of-the-art methods.

Graph contrastive learning (GCL) has become the de-facto approach to conducting self-supervised learning on graphs for its superior performance. However, non-semantic graph augmentation methods prevent it from achieving better performance, and it suffers from vulnerability to graph attacks. To deal with these problems, we propose AEGCL to leverage graph AutoEncoder in Graph Contrastive Learning which directly targets graph property reconstruction to boost GCL effectiveness and robustness. Specifically, AEGCL has two distinctive characteristics, (1) a novel adaptive augmentation strategy based onmotifcentrality is proposed, which leverages semantic significant higher-order graph property; (2) the original attributed graph is decoupled into feature graph and topology graph to extract their dedicated information, and a simpleAttnFuseis proposed to combine the two augmented graphs and the two decoupled graphs. Graph autoencoder can thus be applied to the topology domain and raw attribute domain. Empirically, extensive experiments on benchmark graph datasets show that AEGCL outperforms existing baseline methods in terms of classification accuracy and robustness.

Next-basket recommendation methods focus on the inference of the next basket by considering the corresponding basket sequence. Although many methods have been developed for the task, they usually suffer from data sparsity. The number of interactions between entities is relatively small compared to their huge bases, so it is crucial to mine as much hidden information as possible from the limited historical interactions for prediction. However, the existing methods mainly just treat the next-basket recommendation task as a single-view sequential prediction problem, which leads to the inadequate mining of the information hidden in multiple views, and the mining of other patterns in the historical interactions is neglected, thus making it difficult to learn high-quality representations and limiting the recommendation effect. To alleviate the above issues, we propose a novel method named HapCL for next-basket recommendation, which mines information from multiple views and patterns with the help of polar contrastive learning. A hierarchical module is designed to mine multiple patterns of historical interactions from different views at two levels. In order to mine self-supervised signals, we design a polar contrastive learning module with a novel graph-based augmentation approach. Experiments on three real-world datasets validate the effectiveness of HapCL.

With the rapid development of wireless communication and localization technologies, the easier collection of trajectory data can bring potential data-driven value. Recently, there has been an increasing interest in how to publish trajectory dataset without revealing personal information. However, since the large-scale and real-world sequential trajectory dataset presents a heterogeneous regional distribution, the existing study ignores the relationship between privacy budget allocation and spatial characteristics, resulting in unreasonable continuity and mapping distortion, and thus lowering the utility of the synthetic dataset. To address this problem, we propose a probability distribution model named Adaptive grid-based Weighted Differential Privacy (AWDP). First, trajectories are adaptively discretized into the multi-resolution grid structures to make trajectories more uniformly distributed and less disturbed by the noise. Second, we allocate different weighted budgets for different grids according to density-based regional characteristics. Third, a spatio-temporal continuity maintenance method is designed to solve unrealistic direction- and density-based continuity deviations of synthetic trajectories. An application system is developed for demonstration purposes which is available online athttp://qgailab.com/awdp/. The extensive experiments on three datasets demonstrate that AWDP performs significantly better than the state-of-the-art model in preserving the density distribution of the original trajectories with differential privacy guarantee and high utility.

Feature selection, an essential technique in data mining, is often confined to batch learning or online idealization of data scenarios despite its significance. Existing online feature selection methods have specific assumptions regarding the data stream, such as requiring a fixed feature space with an explicit pattern and complete labeling of samples. Unfortunately, data streams generated in many real scenarios commonly exhibit arbitrarily incomplete feature spaces and scarcity labels, making existing approaches unsuitable for real applications. To fill these gaps, this study proposes a new problem calledOnline Feature Selection with Varying Features Spaces(OFSVF). OFSVF has a three-fold main idea: 1) it leverages Gaussian Copula to model the incomplete feature correlation in a complete latent space, encoded by continuous variables, 2) it employs a novel tree-ensemble-based approach to select the most informative features on-the-fly, and 3) it develops the underlying geometric structure of instances to establish the relationship between unlabeled and labels. Experimental results are documented to demonstrate the feasibility and effectiveness of our proposed method.

Knowledge distillation, a widely adopted model compression technique, distils knowledge from a large teacher model to a smaller student model, with the goal of reducing the computational resources required for the student model. However, most existing distillation approaches focus on the types of knowledge and how to distil them, which neglect the student model's neuronal responses to the knowledge. In this article, we demonstrate that the kullback-leibler loss inhibits the neuronal responses in the opposite gradient direction, which injures the student model's potential during distilling. To address this problem, we introduce a principled dual-stage distillation scheme to rejuvenate all inhibited neurons at the neuronal level. In the first stage, we detect all the neurons in the student model during the standard distillation period and divide them into two parts according to their responses. In the second stage, we propose three strategies to resuscitate the neurons differently, which allows us to exploit the full potential of the student model. Through the experiments in various aspects of knowledge distillation, it is verified that the proposed approach outperforms the current state-of-the-art approaches. Our work provides a neuronal perspective for studying the response of the student model to the knowledge from the teacher model.

Although previous graph-based multi-view clustering (MVC) algorithms have gained significant progress, most of them are still faced with three limitations. First, they often suffer from high computational complexity, which restricts their applications in large-scale scenarios. Second, they usually perform graph learning either at the single-view level or at the view-consensus level, but often neglect the possibility of the joint learning of single-view and consensus graphs. Third, many of them rely on the k -means for discretization of the spectral embeddings, which lack the ability to directly learn the graph with discrete cluster structure. In light of this, this article presents an efficient MVC approach via u nified and d iscrete b ipartite g raph l earning (UDBGL). Specifically, the anchor-based subspace learning is incorporated to learn the view-specific bipartite graphs from multiple views, upon which the bipartite graph fusion is leveraged to learn a view-consensus bipartite graph with adaptive weight learning. Furthermore, the Laplacian rank constraint is imposed to ensure that the fused bipartite graph has discrete cluster structures (with a specific number of connected components). By simultaneously formulating the view-specific bipartite graph learning, the view-consensus bipartite graph learning, and the discrete cluster structure learning into a unified objective function, an efficient minimization algorithm is then designed to tackle this optimization problem and directly achieve a discrete clustering solution without requiring additional partitioning, which notably has linear time complexity in data size. Experiments on a variety of multi-view datasets demonstrate the robustness and efficiency of our UDBGL approach. The code is available at https://github.com/huangdonghere/UDBGL.

As a challenging problem, incomplete multi-view clustering (MVC) has drawn much attention in recent years. Most of the existing methods contain the feature recovering step inevitably to obtain the clustering result of incomplete multi-view datasets. The extra target of recovering the missing feature in the original data space or common subspace is difficult for unsupervised clustering tasks and could accumulate mistakes during the optimization. Moreover, the biased error is not taken into consideration in the previous graph-based methods. The biased error represents the unexpected change of incomplete graph structure, such as the increase in the intra-class relation density and the missing local graph structure of boundary instances. It would mislead those graph-based methods and degrade their final performance. In order to overcome these drawbacks, we propose a new graph-based method named Graph Structure Refining for Incomplete MVC (GSRIMC). GSRIMC avoids recovering feature steps and just fully explores the existing subgraphs of each view to produce superior clustering results. To handle the biased error, the biased error separation is the core step of GSRIMC. In detail, GSRIMC first extracts basic information from the precomputed subgraph of each view and then separates refined graph structure from biased error with the help of tensor nuclear norm. Besides, cross-view graph learning is proposed to capture the missing local graph structure and complete the refined graph structure based on the complementary principle. Extensive experiments show that our method achieves better performance than other state-of-the-art baselines.

Graph learning has emerged as a promising technique for multi-view clustering due to its ability to learn a unified and robust graph from multiple views. However, existing graph learning methods mostly focus on the multi-view consistency issue, yet often neglect the inconsistency between views, which makes them vulnerable to possibly low-quality or noisy datasets. To overcome this limitation, we propose a new multi-view graph learning framework, which for the first time simultaneously and explicitly models multi-view consistency and inconsistency in a unified objective function, through which the consistent and inconsistent parts of each single-view graph as well as the unified graph that fuses the consistent parts can be iteratively learned. Though optimizing the objective function is NP-hard, we design a highly efficient optimization algorithm that can obtain an approximate solution with linear time complexity in the number of edges in the unified graph. Furthermore, our multi-view graph learning approach can be applied to both similarity graphs and dissimilarity graphs, which lead to two graph fusion-based variants in our framework. Experiments on 12 multi-view datasets have demonstrated the robustness and efficiency of the proposed approach. The code is available at https://github.com/youweiliang/Multi-view_Graph_Learning.

Incomplete multiview clustering (IMC) methods have achieved remarkable progress by exploring the complementary information and consensus representation of incomplete multiview data. However, to our best knowledge, none of the existing methods attempts to handle the uncoupled and incomplete data simultaneously, which affects their generalization ability in real-world scenarios. For uncoupled incomplete data, the unclear and partial cross-view correlation introduces the difficulty to explore the complementary information between views, which results in the unpromising clustering performance for the existing multiview clustering methods. Besides, the presence of hyperparameters limits their applications. To fill these gaps, a novel uncoupled IMC (UIMC) method is proposed in this article. Specifically, UIMC develops a joint framework for feature inferring and recoupling. The high-order correlations of all views are explored by performing a tensor singular value decomposition (t-SVD)-based tensor nuclear norm (TNN) on recoupled and inferred self-representation matrices. Moreover, all hyperparameters of the UIMC method are updated in an exploratory manner. Extensive experiments on six widely used real-world datasets have confirmed the superiority of the proposed method in handling the uncoupled incomplete multiview data compared with the state-of-the-art methods.

Community detection has become a prominent task in complex network analysis. However, most of the existing methods for community detection only focus on the lower order structure at the level of individual nodes and edges and ignore the higher order connectivity patterns that characterize the fundamental building blocks within the network. In recent years, researchers have shown interest in motifs and their role in network analysis. However, most of the existing higher order approaches are based on shallow methods, failing to capture the intricate nonlinear relationships between nodes. In order to better fuse higher order and lower order structural information, a novel deep learning framework called motif-based contrastive learning for community detection (MotifCC) is proposed. First, a higher order network is constructed based on motifs. Subnetworks are then obtained by removing isolated nodes, addressing the fragmentation issue in the higher order network. Next, the concept of contrastive learning is applied to effectively fuse various kinds of information from nodes, edges, and higher order and lower order structures. This aims to maximize the similarity of corresponding node information, while distinguishing different nodes and different communities. Finally, based on the community structure of subnetworks, the community labels of all nodes are obtained by using the idea of label propagation. Extensive experiments on real-world datasets validate the effectiveness of MotifCC.

Recent developments in multiagent consensus problems have heightened the role of network topology when the agent number increases largely. The existing works assume that the convergence evolution typically proceeds over a peer-to-peer architecture where agents are treated equally and communicate directly with perceived one-hop neighbors, thus resulting in slower convergence speed. In this article, we first extract the backbone network topology to provide a hierarchical organization over the original multiagent system (MAS). Second, we introduce a geometric convergence method based on the constraint set (CS) under periodically extracted switching-backbone topologies. Finally, we derive a fully decentralized framework named hierarchical switching-backbone MAS (HSBMAS) that is designed to conduct agents converge to a common stable equilibrium. Provable connectivity and convergence guarantees of the framework are provided when the initial topology is connected. Extensive simulation results on different-type and varying-density topologies have shown the superiority of the proposed framework.

In recent years, the gaming industry has flourished. Therefore, game manufacturers need to strive to improve the gaming experience of users in the game. Social recommendation tasks in game scenes have become increasingly important. In this work, we focus on community recommendation scenario. A distinctive feature of game community recommendation is that each member can only belong to one gang for a certain duration, which we refer to as uniqueness of communities. The problem caused by uniqueness is that for users to be recommended, there are no positive samples available for training. The challenge caused by uniqueness is that there are no positive samples available for training when users are recommended. Therefore, the collaborative filtering information between the user and the community is very sparse. Meanwhile, existing methods fail to fully model communities and users based on their features and profiles. To address these problems, we propose Hypergraph Attribute Attention Network (HATT) framework. In order to fully exploit user profiles and similarity between users, we discretize user features into entity nodes and model the heterogeneous relationships between users and communities by hyperedge. We propose a hypergraph attention-based message passing mechanism to capture the high-order relationships and obtain embedding with more semantics. At last, we design contrastive learning paradigms to enhance the model’s representation ability and apply a multi task training strategy to train the model. Extensive experiments on two real world game datasets are conducted and the results demonstrate the superiority of our method in community recommendation.

Bundle recommendation focuses on recommending users with associated item sets at once. Recently, some works utilize Graph Neural Network (GNN), which has a solid power for mining information behind the topological structure, to enhance bundle recommendation performance. The previous GNN-based methods focus on designing the bundle-item association mechanism and fusing the extra information from the item view into the final prediction. However, the crucial component in GNN, namely the neighborhood aggregation mechanism, is yet to be explored under the bundle recommendation scenario. In this work, we propose a bundle-specific neighborhood aggregation mechanism named Auto Graph Filtering (AGF). The AGF refines the neighborhood aggregation mechanism from two aspects. (1) AGF utilizes the 2-hop meta paths in the bundle recommendation scenario instead of user interactions directly, which alleviates the extreme sparsity in the user-bundle graph. (2) AGF automatically reweights all the meta paths during the training. With training procedure completed, AGF optimizes the user-bundle graph to meet the bundle recommendation requirement. The experimental results show that our simplest AGF version, AGFN, consistently outperforms all the baselines. Moreover, the user-bundle graph learned by AGFN could also boost the existing GNN-based methods to achieve a better performance.

With the rapid development of modern communication technology, telecom fraud has been increasing year by year. If fraudsters can be accurately identified before they carry out their scams, it can not only protect people from potential losses but also increase trust in telecom operators. Therefore, in recent years, telecom fraud detection has garnered widespread attention in both academia and industry. Although existing methods for telecom fraud detection have achieved good performance, there are still many unresolved issues for real-world telecom operators. First, existing methods only focus on a single telecom scenario, while real-world telecom scenarios are diverse. Utilizing the characteristics of these different telecom scenarios can improve the effectiveness of telecom fraud detection. Second, existing methods usually use Graph Neural Networks (GNNs) to aggregate neighbor information. However, real-world telecom operators can’t obtain information of users from other operators, resulting in the lacking destination node attributes, which degenerates the performance of GNNs. To address the above issues, in this paper, we propose a new model for Telecom Fraud Detection Based on Feature binning and Autoencoder (TFD-FA). In TFD-FA, a feature binning framework is designed to partition users into different telecom scenarios in order to reflect their unique characteristics. An autoencoder component is also designed to aggregate neighbor information. Furthermore, an imbalance classifier component is constructed to solve the problem of the significantly lower number of fraudsters compared to normal users. Extensive experiments in a real-world dataset demonstrate the effectiveness of TFD-FA, which outperforms the compared baseline models.

With the rapid growth of online platforms and the abundance of available information, personalized recommender systems have become essential for assisting users in discovering relevant and interesting content. Among the various methods, knowledge-aware recommendation model has achieved notable success by leveraging the rich semantic information encoded in knowledge graphs. However, it overlooks the fact that users’ historical click sequences can better reflect their preferences within a period of time, thus imposing certain limitations on the recommendation performance. On the other hand, the application of pre-trained language models in recommender systems has demonstrated increasingly significant potential, as they can capture sequential patterns and dependencies within users’ historical click sequences and effectively capture contextual information in user-item interactions. To this end, we propose a hybrid recommendation model that leverages Pre-training in the collaborative Knowledge graph Attention neTwork (PKAT), to extract both the high-order connectivity information in collaborative knowledge graphs and the contextual information in users’ historical click sequences captured by Bidirectional Encoder Representations from Transformers (BERT). The collaborative knowledge graph attention network enables the model to effectively capture the intricate relationships between users, items, and knowledge entities, thus enhancing the representation learning process. Furthermore, what sets PKAT apart from other state-of-the-art knowledge-aware recommendation methods is the incorporation of the BERT language model. This integration allows PKAT to capture the contextual sequence information of user behavior, enabling it to generate more accurate and personalized recommendations. Extensive experiments are conducted on multiple benchmark datasets. And the results demonstrate that our PKAT model outperforms several state-of-the-art baselines.

In recent years, various recommendation methods are proposed to capture user preferences more accurately, with the assumption that different types of records reflects the positive intention of users to buy items in different degree. However, the records of different channels may denote positive or negative impacts on users’ willingness to buy items in multi-channel scenario, which is a salient features of games. Making recommendation only with the records of buy channel makes it difficult to capture cross-channel impact of items. To solve the issue, this paper proposes a multi-channel gated gifts recommendation method, named MCRec, which is able to mine the impact of acquisition in different channels on buy channel from multi-channel records and generate personalized gifts for users. The MCRec method extracts channel-aware correlation of items from channel-aware item-item graphs. The contribution of different items in various sessions is distinguished with item gate, and the impact of the context of different channels on buy channel is measured with channel gate. Finally, personalized gifts will be generated hierarchically with different purposes. Extensive experiments are conducted on two datasets that are constructed with the data collected from two massively multiplayer online (MMO) games. The results demonstrate the superiority of our MCRec over state-of-the-art recommendation methods in gifts recommendation. Further ablation studies validate the effectiveness of the design of MCRec in modeling cross-channel impact of items.

Class imbalance is the phenomenon that some classes have much fewer instances than others, which is ubiquitous in real-world graph-structured scenarios. Recent studies find that off-the-shelf Graph Neural Networks (GNNs) would under-represent minor class samples. We investigate this phenomenon and discover that the subspaces of minor classes being squeezed by those of the major ones in the latent space is the main cause of this failure. We are naturally inspired to enlarge the decision boundaries of minor classes and propose a general framework GraphSHA by Synthesizing HArder minor samples. Furthermore, to avoid the enlarged minor boundary violating the subspaces of neighbor classes, we also propose a module called SemiMixup to transmit enlarged boundary information to the interior of the minor classes while blocking information propagation from minor classes to neighbor classes. Empirically, GraphSHA shows its effectiveness in enlarging the decision boundaries of minor classes, as it outperforms various baseline methods in class-imbalanced node classification with different GNN backbone encoders over seven public benchmark datasets. Code is avilable at https://github.com/wenzhilics/GraphSHA.

Contrastive learning (CL) has become the de-facto learning paradigm in self-supervised learning on graphs, which generally follows the "augmenting-contrasting'' learning scheme. However, we observe that unlike CL in computer vision domain, CL in graph domain performs decently even without augmentation. We conduct a systematic analysis of this phenomenon and argue that homophily, i.e., the principle that "like attracts like'', plays a key role in the success of graph CL. Inspired to leverage this property explicitly, we propose HomoGCL, a model-agnostic framework to expand the positive set using neighbor nodes with neighbor-specific significances. Theoretically, HomoGCL introduces a stricter lower bound of the mutual information between raw node features and node embeddings in augmented views. Furthermore, HomoGCL can be combined with existing graph CL models in a plug-and-play way with light extra computational overhead. Extensive experiments demonstrate that HomoGCL yields multiple state-of-the-art results across six public datasets and consistently brings notable performance improvements when applied to various graph CL methods. Code is avilable at https://github.com/wenzhilics/HomoGCL.

Ensemble clustering has shown its promising ability in fusing multiple base clusterings into a probably better and more robust clustering result. Typically, the co-association matrix based ensemble clustering methods attempt to integrate multiple connective matrices from base clusterings by weighted fusion to acquire a common graph representation. However, few of them are aware of the potential noise or corruption from the common representation by direct integration of different connective matrices with distinct cluster structures, and further consider the mutual information propagation between the input observations. In this paper, we propose a Graph Tensor Learning based Ensemble Clustering (GTLEC) method to refine multiple connective matrices by the substantial rank recovery and graph tensor learning. Within this framework, each input connective matrix is dexterously refined to approximate a graph structure by obeying the theoretical rank constraint with an adaptive weight coefficient. Further, we stack multiple refined connective matrices into a three-order tensor to extract their higher-order similarities via graph tensor learning, where the mutual information propagation across different graph matrices will also be promoted. Extensive experiments on several challenging datasets have confirmed the superiority of GTLEC compared with the state-of-the-art.

Pre-trained language models (PLMs) have demonstrated strong performance in sequential recommendation (SR), which are utilized to extract general knowledge. However, existing methods still lack domain knowledge and struggle to capture users' fine-grained preferences. Meanwhile, many traditional SR methods improve this issue by integrating side information while suffering from information loss. To summarize, we believe that a good recommendation system should utilize both general and domain knowledge simultaneously. Therefore, we introduce an external knowledge base and propose Knowledge Prompt-tuning for Sequential Recommendation (KP4SR). Specifically, we construct a set of relationship templates and transform a structured knowledge graph (KG) into knowledge prompts to solve the problem of the semantic gap. However, knowledge prompts disrupt the original data structure and introduce a significant amount of noise. We further construct a knowledge tree and propose a knowledge tree mask, which restores the data structure in a mask matrix form, thus mitigating the noise problem. We evaluate KP4SR on three real-world datasets, and experimental results show that our approach outperforms state-of-the-art methods on multiple evaluation metrics. Specifically, compared with PLM-based methods, our method improves NDCG@5 and HR@5 by 40.65% and 36.42% on the books dataset, 11.17% and 11.47% on the music dataset, and 22.17% and 19.14% on the movies dataset, respectively. Our code is publicly available at the link: https://github.com/zhaijianyang/KP4SR.

Nowadays, it is more and more convenient for people to participate in group activities. Therefore, providing some recommendations to groups of individuals is indispensable. Group recommendation is the task of suggesting items or events for a group of users in social networks or online communities. In this work, we study group recommendation in a particular scenario, namely occasional group recommendation, which has few or no historical directly interacted items. Existing group recommendation methods mostly adopt attention-based preference aggregation strategies to capture group preferences. However, these models either ignore the complex high-order interactions between groups, users and items or greatly reduce the efficiency by introducing complex data structures. Moreover, occasional group recommendation suffers from the problem of data sparsity due to the lack of historical group-item interactions. In this work, we focus on addressing the aforementioned challenges and propose a novel group recommendation model called Self-Supervised Group Graph Collaborative Filtering (SGGCF). The goal of the model is capturing the high-order interactions between users, items and groups and alleviating the data sparsity issue in an efficient way. First, we explicitly model the complex relationships as a unified user-centered heterogeneous graph and devise a base group recommendation model. Second, we explore self-supervised learning on the graph with two kinds of contrastive learning module to capture the implicit relations between groups and items. At last, we treat the proposed contrastive learning loss as supplementary and apply a multi-task strategy to jointly train the BPR loss and the proposed contrastive learning loss. We conduct extensive experiments on three real-world datasets, and the experimental results demonstrate the superiority of our proposed model in comparison to the state-of-the-art baselines.

Abstract Graph collaborative filtering methods have shown great performance improvements compared with deep neural network-based models. However, these methods suffer from data sparsity and data noise problems. To address these issues, we propose a new contrastive learning-based graph collaborative filtering method to learn more robust representations. The proposed method is called signal contrastive enhanced graph collaborative filtering (SC-GCF), which conducts contrastive learning on graph signals. It has been proved that graph neural networks correspond to low-pass filters on the graph signals from the graph convolution perspective. Different from the previous contrastive learning-based methods, we first pay attention to the diversity of graph signals to directly optimize the informativeness of the graph signals. We introduce a hypergraph module to strengthen the representation learning ability of graph neural networks. The hypergraph learning module utilizes a learnable hypergraph structure to model the latent global dependency relations that graph neural networks cannot depict. Experiments are conducted on four public datasets, and the results show significant improvements compared with the state-of-the-art methods, which confirms the importance of considering signal-level contrastive learning and hypergraph learning.

Academic knowledge graph is an important application of knowledge graph in the vertical field of academia. At present, the construction of the academic knowledge graph is mainly completed by extracting published academic papers, authors, publications, and other information from related databases. However, academic information is not just information of published papers. Scholars’ academic activities include participation in academic conferences, visiting and making presentation, and so on. However, the above academic information is hidden in natural language texts and cannot be directly stored in academic knowledge graph. This article proposes an approach named construct-SCHOLAT knowledge graph to construct an academic event knowledge graph based on academic social network SCHOLAT. The construction framework mainly consists of two parts: data preprocessing and event extraction. In the data preprocessing, we propose a knowledge graph embedding method to represent scholars’ academic social feature. In the event extraction, we concatenate the preprocessed scholar vector with academic we-media blog text into the extraction model based on BiLSTM-CRF fused with attention mechanism. The extracted events are added to academic knowledge graph, and a public relationship exists between the event and the scholar. Compared to the previous methods, our framework has an excellent performance after experimental verification. To the best of our knowledge, this is the first study to use the scholar academic social information of the scholar who edited the text as the event extraction input information. In addition, we publish a Chinese event extraction dataset SCHOLAT academic event extraction.1The dataset includes academic we-media blog and the social behavior embedding vector of the scholar. All the data in this dataset are derived from the academic social network SCHOLAT.1https://www.scholat.com/research/opendata

Multiparty learning is an indispensable technique to improve the learning performance via integrating data from multiple parties. Unfortunately, directly integrating multiparty data could not meet the privacy-preserving requirements, which then induces the development of privacy-preserving machine learning (PPML), a key research task in multiparty learning. Despite this, the existing PPML methods generally cannot simultaneously meet multiple requirements, such as security, accuracy, efficiency, and application scope. To deal with the aforementioned problems, in this article, we present a new PPML method based on the secure multiparty interactive protocol, namely, the multiparty secure broad learning system (MSBLS) and derive its security analysis. To be specific, the proposed method employs the interactive protocol and random mapping to generate the mapped features of data, and then uses efficient broad learning to train the neural network classifier. To the best of our knowledge, this is the first attempt for privacy computing method that jointly combines secure multiparty computing and neural network. Theoretically, this method can ensure that the accuracy of the model will not be reduced due to encryption, and the calculation speed is very fast. Three classical datasets are adopted to verify our conclusion.

Community detection in multiview networks has drawn an increasing amount of attention in recent years. Many approaches have been developed from different perspectives. Despite the success, the problem of community detection in adversarial multiview networks remains largely unsolved. An adversarial multiview network is a multiview network that suffers an adversarial attack on community detection in which the attackers may deliberately remove some critical edges so as to hide the underlying community structure, leading to the performance degeneration of the existing approaches. To address this problem, we propose a novel approach, called higher order connection enhanced multiview modularity (HCEMM). The main idea lies in enhancing the intracommunity connection of each view by means of utilizing the higher order connection structure. The first step is to discover the view-specific higher order Microcommunities (VHM-communities) from the higher order connection structure. Then, for each view of the original multiview network, additional edges are added to make the nodes in each of its VHM-communities fully connected like a clique, by which the intracommunity connection of the multiview network can be enhanced. Therefore, the proposed approach is able to discover the underlying community structure in a multiview network while recovering the missing edges. Extensive experiments conducted on 16 real-world datasets confirm the effectiveness of the proposed approach.

More and more social platforms suffer from the spam messages and trouble in detecting them. Lizhi, one of the most famous audio APPs in China, also suffers from the spam messages. The spam detector in Lizhi faces two major challenges: adversarial actions taken by the spammers and lack of labeled data. In this article, we propose a novel adversarial spam detector based on character similarity network for detecting adversarial spam messages in Lizhi, namely Lizhi adversarial spam detector. The character similarity network is designed to solve the adversarial actions before being taken by the spammers, and the character embedding model is proposed to learn the embeddings of all the characters in the corpus. Then, the model generates the sentence embedding of the messages sent by users. At last the classifier will predict whether the message is a spam. Also, the model utilizes active learning to solve the problem of lack of labeled data. Both of offline and online experiments are conducted to confirm the effectiveness of the proposed method.

Electroencephalogram (EEG) is an important technology to explore the central nervous mechanism of tinnitus. However, it is hard to obtain consistent results in many previous studies for the high heterogeneity of tinnitus. In order to identify tinnitus and provide theoretical guidance for the diagnosis and treatment, we propose a robust, data-efficient multi-task learning framework called Multi-band EEG Contrastive Representation Learning (MECRL). In this study, we collect resting-state EEG data from 187 tinnitus patients and 80 healthy subjects to generate a high-quality large-scale EEG dataset on tinnitus diagnosis, and then apply the MECRL framework on the generated dataset to obtain a deep neural network model which can distinguish tinnitus patients from the healthy controls accurately. Subject-independent tinnitus diagnosis experiments are conducted and the result shows that the proposed MECRL method is significantly superior to other state-of-the-art baselines and can be well generalized to unseen topics. Meanwhile, visual experiments on key parameters of the model indicate that the high-classification weight electrodes of tinnitus' EEG signals are mainly distributed in the frontal, parietal and temporal regions. In conclusion, this study facilitates our understanding of the relationship between electrophysiology and pathophysiology changes of tinnitus and provides a new deep learning method (MECRL) to identify the neuronal biomarkers in tinnitus.

Graph-oriented multi-view clustering methods have achieved impressive performances by employing relationships and complex structures hidden in multi-view data. However, most of them still suffer from the following two common problems. (1) They target at studying a common representation or pairwise correlations between views, neglecting the comprehensiveness and deeper higher-order correlations among multiple views. (2) The prior knowledge of view-specific representation can not be taken into account to obtain the consensus indicator graph in a unified graph construction and clustering framework. To deal with these problems, we propose a novel Low-rank Tensor Based Proximity Learning (LTBPL) approach for multi-view clustering, where multiple low-rank probability affinity matrices and consensus indicator graph reflecting the final performances are jointly studied in a unified framework. Specifically, multiple affinity representations are stacked in a low-rank constrained tensor to recover their comprehensiveness and higher-order correlations. Meanwhile, view-specific representation carrying different adaptive confidences is jointly linked with the consensus indicator graph. Extensive experiments on nine real-world datasets indicate the superiority of LTBPL compared with the state-of-the-art methods.

Despite significant progress, there remain three limitations to the previous multi-view clustering algorithms. First, they often suffer from high computational complexity, restricting their feasibility for large-scale datasets. Second, they typically fuse multi-view information via one-stage fusion, neglecting the possibilities in multi-stage fusions. Third, dataset-specific hyperparameter-tuning is frequently required, further undermining their practicability. In light of this, we propose afastmulti-viewclustering viaensembles (FastMICE) approach. Particularly, the concept of random view groups is presented to capture the versatile view-wise relationships, through which the hybrid early-late fusion strategy is designed to enable efficient multi-stage fusions. Withmultipleviews extended tomanyview groups, three levels of diversity (w.r.t. features, anchors, and neighbors, respectively) are jointly leveraged for constructing the view-sharing bipartite graphs in the early-stage fusion. Then, a set of diversified base clusterings for different view groups are obtained via fast graph partitioning, which are further formulated into a unified bipartite graph for final clustering in the late-stage fusion. Notably, FastMICE has almost linear time and space complexity, and is free of dataset-specific tuning. Experiments on 22 multi-view datasets demonstrate its advantages in scalability (for extremely large datasets), superiority (in clustering performance), and simplicity (to be applied) over the state-of-the-art. Code available:https://github.com/huangdonghere/FastMICE.

Anomaly detection on attributed networks has received an increasing amount of attention in recent years. Despite the success, most of the existing methods only focus on detecting the abnormal nodes while fail to detect the abnormal subgraphs. In this paper, we define a new problem of hybrid-order anomaly detection on attributed networks, which aims to detect both of the abnormal nodes and subgraphs. To this end, a new deep learning model called Hybrid-Order Graph Attention Network (HO-GAT) is developed, which is able to simultaneously detect the abnormal nodes and motif instances in an attributed network. In order to model the mutual influence between nodes and motif instances, the learning procedures of the node representation and the motif instance representation are integrated into a unified graph attention network with a novel hybrid-order self-attention mechanism. After learning the node representation and the motif instance representation, two decoders are respectively designed to reconstruct the attribute information of the nodes and motif instances, and the hybrid-order topological structure among nodes and motif instances. And finally, the reconstruction errors are utilized as the abnormal score of nodes and motif instances respectively. Extensive experiments conducted on real-world datasets have confirmed the effectiveness of the HO-GAT method.

Graph neural network (GNN) has shown its prominent performance in representation learning of graphs but it has not been fully considered for heterogeneous graphs which contain more complex structures and rich semantics. The rich semantic information of heterogeneous graph can be usually revealed by meta-paths. Therefore, most of the existing GNN models designed for heterogeneous graphs utilize the meta-path based neighborhood sampler to divide a heterogeneous graph into multiple homogeneous subgraphs according to various meta-paths so that the homogeneous GNN can be applied to investigate heterogeneous graphs. Nevertheless, the way of embedding semantic information of meta-paths into multiple homogeneous graphs isimplicitand ineffective, which cannot accurately capture the semantics of heterogeneous graphs. In this paper, we propose a novel semi-supervised GNN model namedExplicitMessage-Passing Heterogeneous Graph Neural Network (EMP), which executes the process ofexplicitmessage-passing along the meta-paths. Besides, we also propose a split method for meta-paths and consider mutual effect between various meta-paths in advance in the proposed model, so that the semantic information of the whole set of meta-paths can be captured accurately. Extensive experiments conducted on three real-world datasets demonstrate the superiority of the proposed model.

Session-based recommendation tries to make use of anonymous session data to deliver high-quality recommendations under the condition that user profiles and the complete historical behavioral data of a target user are unavailable. Previous works consider each session individually and try to capture user interests within a session. Despite their encouraging results, these models can only perceive intra-session items and cannot draw upon the massive historical relational information. To solve this problem, we propose a novel method named global graph guided session-based recommendation (G3SR). G3SR decomposes the session-based recommendation workflow into two steps. First, a global graph is built upon all session data, from which the global item representations are learned in an unsupervised manner. Then, these representations are refined on session graphs under the graph networks, and a readout function is used to generate session representations for each session. Extensive experiments on two real-world benchmark datasets show remarkable and consistent improvements of the G3SR method over the state-of-the-art methods, especially for cold items.

A variety of methods have been proposed for modeling and mining dynamic complex networks, in which the topological structure varies with time. As the most popular and successful network model, the stochastic block model (SBM) has been extended and applied to community detection, link prediction, anomaly detection, and evolution analysis of dynamic networks. However, all current models based on the SBM for modeling dynamic networks are designed at the community level, assuming that nodes in each community have the same dynamic behavior, which usually results in poor performance on temporal community detection and loses the modeling of node abnormal behavior. To solve the above-mentioned problem, this article proposes a hierarchical Bayesian dynamic SBM (HB-DSBM) for modeling the node-level and community-level dynamic behavior in a dynamic network synchronously. Based on the SBM, we introduce a hierarchical Dirichlet generative mechanism to associate the global community evolution with the microscopic transition behavior of nodes near-perfectly and generate the observed links across the dynamic networks. Meanwhile, an effective variational inference algorithm is developed and we can easy to infer the communities and dynamic behaviors of the nodes. Furthermore, with the two-level evolution behaviors, it can identify nodes or communities with abnormal behavior. Experiments on simulated and real-world networks demonstrate that HB-DSBM has achieved state-of-the-art performance on community detection and evolution. In addition, abnormal evolutionary behavior and events on dynamic networks can be effectively identified by our model.

Most existing multiview clustering methods are based on the original feature space. However, the feature redundancy and noise in the original feature space limit their clustering performance. Aiming at addressing this problem, some multiview clustering methods learn the latent data representation linearly, while performance may decline if the relation between the latent data representation and the original data is nonlinear. The other methods which nonlinearly learn the latent data representation usually conduct the latent representation learning and clustering separately, resulting in that the latent data representation might be not well adapted to clustering. Furthermore, none of them model the intercluster relation and intracluster correlation of data points, which limits the quality of the learned latent data representation and therefore influences the clustering performance. To solve these problems, this article proposes a novel multiview clustering method via proximity learning in latent representation space, named multiview latent proximity learning (MLPL). For one thing, MLPL learns the latent data representation in a nonlinear manner which takes the intercluster relation and intracluster correlation into consideration simultaneously. For another, through conducting the latent representation learning and consensus proximity learning simultaneously, MLPL learns a consensus proximity matrix with k connected components to output the clustering result directly. Extensive experiments are conducted on seven real-world datasets to demonstrate the effectiveness and superiority of the MLPL method compared with the state-of-the-art multiview clustering methods.

Most existing recommendation research has been concentrated on unidirectional recommendation, i.e. only recommending items to users. However, in many real-world scenarios, the platform needs to achieve bi-directional recommendation. For example, in an online recruitment scenario, the recommender system not only needs to recommend positions to candidates, but also recommend candidates to enterprises. In this paper, we first formalize a new recommendation problem called bi-directional recommendation and contribute a new bidirectional recommendation model named BiROR (Bi-directional Recommendation for Online Recruitment). In BiROR, an encoder component is utilized to learn the text embeddings, and a graph learning component is designed to learn the graph embeddings. In addition, a multi-task learning framework is designed to achieve bi-directional recommendation. In the multi-task learning framework, we share the text embeddings and graph embeddings to alleviate the problems of data sparsity and data asymmetry in online recruitment. Extensive experiments in a real-world task show that BiROR outperforms the state-of-the-art methods, verifying the effectiveness of the designs of our model.

Multi-view subspace clustering targets at clustering data lying in a union of low-dimensional subspaces. Generally, an n X n affinity graph is constructed, on which spectral clustering is then performed to achieve the final clustering. Both graph construction and graph partitioning of spectral clustering suffer from quadratic or even cubic time and space complexity, leading to difficulty in clustering large-scale datasets. Some efforts have recently been made to capture data distribution in multiple views by selecting key anchor bases beforehand with k-means or uniform sampling strategy. Nevertheless, few of them pay attention to the algebraic property of the anchors. How to learn a set of high-quality orthogonal bases in a unified framework, while maintaining its scalability for very large datasets, remains a big challenge. In view of this, we propose an Efficient Orthogonal Multi-view Subspace Clustering (OMSC) model with almost linear complexity. Specifically, the anchor learning, graph construction and partition are jointly modeled in a unified framework. With the mutual enhancement of each other, a more discriminative and flexible anchor representation and cluster indicator can be jointly obtained. An alternate minimizing strategy is developed to deal with the optimization problem, which is proved to have linear time complexity w.r.t. the sample number. Extensive experiments have been conducted to confirm the superiority of the proposed OMSC method. The source codes and data are available at https://github.com/ManshengChen/Code-for-OMSC-master.

Multiview clustering has been extensively studied to take advantage of multi-source information to improve the clustering performance. In general, most of the existing works typically compute an $n\times n$ affinity graph by some similarity/distance metrics (e.g. the Euclidean distance) or learned representations, and explore the pairwise correlations across views. But unfortunately, a quadratic or even cubic complexity is often needed, bringing about difficulty in clustering large-scale datasets. Some efforts have been made recently to capture data distribution in multiple views by selecting view-wise anchor representations with k-means, or by direct matrix factorization on the original observations. Despite the significant success, few of them have considered the view-insufficiency issue, implicitly holding the assumption that each individual view is sufficient to recover the cluster structure. Moreover, the latent integral space as well as the shared cluster structure from multiple insufficient views is not able to be simultaneously discovered. In view of this, we propose an \underlineA daptively-weighted \underlineI ntegral Space for Fast \underlineM ultiview \underlineC lustering (AIMC) with nearly linear complexity. Specifically, view generation models are designed to reconstruct the view observations from the latent integral space with diverse adaptive contributions. Meanwhile, a centroid representation with orthogonality constraint and cluster partition are seamlessly constructed to approximate the latent integral space. An alternate minimizing algorithm is developed to solve the optimization problem, which is proved to have linear time complexityw.r.t. the sample size. Extensive experiments conducted on several real-world datasets confirm the superiority of the proposed AIMC method compared with the state-of-the-art methods.

Abstract Multi-view clustering (MVC) has attracted more and more attention in the recent few years by making full use of complementary and consensus information between multiple views to cluster objects into different partitions. Although there have been two existing works for MVC survey, neither of them jointly takes the recent popular deep learning-based methods into consideration. Therefore, in this paper, we conduct a comprehensive survey of MVC from the perspective of representation learning. It covers a quantity of multi-view clustering methods including the deep learning-based models, providing a novel taxonomy of the MVC algorithms. Furthermore, the representation learning-based MVC methods can be mainly divided into two categories, i.e., shallow representation learning-based MVC and deep representation learning-based MVC, where the deep learning-based models are capable of handling more complex data structure as well as showing better expression. In the shallow category, according to the means of representation learning, we further split it into two groups, i.e., multi-view graph clustering and multi-view subspace clustering. To be more comprehensive, basic research materials of MVC are provided for readers, containing introductions of the commonly used multi-view datasets with the download link and the open source code library. In the end, some open problems are pointed out for further investigation and development.

Recently, hybrid-order community detection has been proposed for addressing the hypergraph fragmentation issue suffered by the motif-based higher-order community detection. However, the existing attempts of hybrid-order community detection inadvertently damage the lower-order connectivity pattern and the higher-order connectivity pattern when constructing the fusion model. Additionally, like the higher-order community detection approaches, they also adopt a two-phase strategy that separately applies the existing graph node clustering methods to the proximity matrix derived from the lower-order connectivity pattern and the higher-order connectivity pattern. Therefore, the higher-order connectivity pattern is only utilized for constructing proximity matrix and hence has no direct effect on the final community results. In this paper, to address the above issues, we propose a Generative model for Hybrid-Order Community detection (GHOC). The main idea lies in defining a likelihood function of a generative model that finds the optimal community membership strength vectors of nodes, based on which the original lower-order connectivity pattern and the higher-order connectivity pattern can be directly reconstructed simultaneously. From the community membership strength vectors, the final community structure can be derived. Extensive experiments have been conducted on several data sets, and the results have confirmed the superiority of the proposed GHOC method.

Multiview subspace clustering (MVSC) is a recently emerging technique that aims to discover the underlying subspace in multiview data and thereby cluster the data based on the learned subspace. Though quite a few MVSC methods have been proposed in recent years, most of them cannot explicitly preserve the locality in the learned subspaces and also neglect the subspacewise grouping effect, which restricts their ability of multiview subspace learning. To address this, in this article, we propose a novel MVSC with grouping effect (MvSCGE) approach. Particularly, our approach simultaneously learns the multiple subspace representations for multiple views with smooth regularization, and then exploits the subspacewise grouping effect in these learned subspaces by means of a unified optimization framework. Meanwhile, the proposed approach is able to ensure the cross-view consistency and learn a consistent cluster indicator matrix for the final clustering results. Extensive experiments on several benchmark datasets have been conducted to validate the superiority of the proposed approach.

The rapid emergence of high-dimensional data in various areas has brought new challenges to current ensemble clustering research. To deal with the curse of dimensionality, recently considerable efforts in ensemble clustering have been made by means of different subspace-based techniques. However, besides the emphasis on subspaces, rather limited attention has been paid to the potential diversity in similarity/dissimilarity metrics. It remains a surprisingly open problem in ensemble clustering how to create and aggregate a large population of diversified metrics, and furthermore, how to jointly investigate the multilevel diversity in the large populations of metrics, subspaces, and clusters in a unified framework. To tackle this problem, this article proposes a novel multidiversified ensemble clustering approach. In particular, we create a large number of diversified metrics by randomizing a scaled exponential similarity kernel, which are then coupled with random subspaces to form a large set of metric-subspace pairs. Based on the similarity matrices derived from these metric-subspace pairs, an ensemble of diversified base clusterings can be thereby constructed. Furthermore, an entropy-based criterion is utilized to explore the cluster wise diversity in ensembles, based on which three specific ensemble clustering algorithms are presented by incorporating three types of consensus functions. Extensive experiments are conducted on 30 high-dimensional datasets, including 18 cancer gene expression datasets and 12 image/speech datasets, which demonstrate the superiority of our algorithms over the state of the art. The source code is available at https://github.com/huangdonghere/MDEC.

Network embedding aims to learn the low-dimensional node representations for networks, which has attracted an increasing amount of attention in recent years. Most existing efforts in this field attempt to embed the network based on node similarity, which generally relies on edge existence statistics of the network. Instead of relying on the global edge existence statistics for every node pair, in this article, we utilize the information between a pair of nodes in a local way and propose a model, called node pair information preserving network embedding (NINE), based on adversarial networks. The main idea lies in preserving the node pair information (NI) by means of adversarial networks. The architecture of the proposed NINE model consists of three main components, namely: 1) NI embedder; 2) NI generator; and 3) NI discriminator. In the NI embedder, to avoid the complicated similarity calculation for a pair of nodes, the original NI vector calculated from the direct neighbor information of the two nodes is adopted as features, and the edge existence information is taken as labels to learn the embedded NI vector in a supervised learning manner. The second component is the NI generator, which takes the original node representation vectors of a node pair as input and outputs the generated NI vector. In order to make the generated NI vector follow the same distribution of the corresponding embedded NI vector, the generative adversarial network (GAN) is adopted, resulting in the third component, called the NI discriminator. Extensive experiments are conducted on seven real-world datasets in three downstream tasks, namely: 1) network reconstruction; 2) link prediction; and 3) node classification. Comparison results with seven state-of-the-art models demonstrate the effectiveness, efficiency, and rationality of our model.

In recent years, the recommender system has been widely used in online platforms, which can extract useful information from giant volumes of data and recommend suitable items to the user according to user preferences. However, the recommender system usually suffers from sparsity and cold-start problems. Cross-domain recommendation, as a particular example of transfer learning, has been used to solve the aforementioned problems. However, many existing cross-domain recommendation approaches are based on matrix factorization, which can only learn the shallow and linear characteristics of users and items. Therefore, in this article, we propose a novel autoencoder framework with an attention mechanism (AAM) for cross-domain recommendation, which can transfer and fuse information between different domains and make a more accurate rating prediction. The main idea of the proposed framework lies in utilizing autoencoder, multilayer perceptron, and self-attention to extract user and item features, learn the user and item-latent factors, and fuse the user-latent factors from different domains, respectively. In addition, to learn the affinity of the user-latent factors between different domains in a multiaspect level, we also strengthen the self-attention mechanism by using multihead self-attention and propose AAM++. Experiments conducted on two real-world datasets empirically demonstrate that our proposed methods outperform the state-of-the-art methods in cross-domain recommendation and AAM++ performs better than AAM on sparse and large-scale datasets.

Considering sessions as directed subgraphs, graph neural networks (GNNs) are supposed to be capable of capturing the complex dependencies among items and suitable for session-based recommendation. However, deep GNNs suffer from the oversmoothing problem of making all nodes converge to the same value. In session-based recommendation, the subgraphs transformed by short sessions are usually simple, which cause worse oversmoothing problem. To apply GNNs to session-based recommendation sufficiently, in this article, we propose a hybrid-order gated GNN (HGNN) on account of the oversmoothing problem. The proposed HGNN model is based on the hybrid-order propagation, which avoids insignificant patterns and captures complex dependencies in propagation. What's more, the attention mechanism is utilized to learn different weights of orders in propagation. Then, HGNN is applied to session-based recommendation, which results in a new method called SR-HGNN. Experimental results show that SR-HGNN outperforms the state-of-the-art session-based recommendation methods and eases the oversmoothing problem.

Bearing fault diagnosis is of great significance to the reliability and stability of modern petrochemical systems. The existing dimensionless index-based bearing fault diagnosis methods suffer from several shortcomings, which are associated with excessive dependence on expert knowledge, insufficient sensitivity in fault feature extraction, and low diagnostic accuracy of nonstationary nonlinear dynamic signals. In this article, a data preprocessing by mutual dimensionless and similar Gram matrix in fault diagnosis is proposed. In this preprocessing, the vibration signal of bearing fault is treated by the mutual dimensionless theory and similar Gram matrix, which is further integrated with the convolutional neural network. The proposed method is tested on two datasets, including a multistage centrifugal fan dataset from our laboratory and a motor bearing dataset from the Case Western Reserve University, achieving an average prediction accuracy of 89.65% and 97.21%, respectively, while reducing the training time significantly. Moreover, the proposed method is compared with other deep learning and traditional methods, including recurrent neural network, support vector machines, and multigenetic algorithm. The experimental results demonstrated that this method can effectively identify fault types by combining various intelligent methods, and compared to traditional dimensionless fault diagnosis methods, the average diagnosis accuracy is significantly improved. The results are also compared with results reported in the literature, indicating that the proposed method can improve fault diagnosis accuracy in an effective and stable way.

Most proximity-based multi-view clustering methods are sensitive to the initial proximity matrix, where the clustering performance is quite unstable when using different initial proximity matrixes. This problem is defined as the initial value sensitivity problem. Since clustering is an unsupervised learning task, it is unrealistic to tune the initial proximity matrix. Thus, how to overcome the initial value sensitivity problem is a significant but unsolved issue in the proximity-based multi-view clustering. To this end, this paper proposes a novel multi-view proximity learning method, named multi-view consensus proximity learning (MCPL). On the one hand, by integrating the information of all views in a self-weighted manner and giving a rank constraint on the Laplacian matrix, the MCPL method learns the consensus proximity matrix to directly reflect the clustering result. On the other hand, different from most multi-view proximity learning methods, in the proposed MCPL method, the data representatives rather than the original data objects are adopted to learn the consensus proximity matrix. The data representatives will be updated in the process of the proximity learning so as to weaken the impact of the initial value on the clustering performance. Extensive experiments are conducted to demonstrate the effectiveness of the proposed method.

Recently, some attempts have been made in introducing deep neural networks (DNNs) to recommender systems for generating more accurate prediction due to the nonlinear representation learning capability of DNNs. However, they inevitably result in high computational and storage costs. Worse still, due to the relatively small number of ratings that can be fed into DNNs, they may easily suffer from the overfitting issue. To tackle these issues, we propose a novel recommendation framework based on Back Propagation (BP) neural network with attention mechanism, namely BPAM++. In particular, the BP neural network is utilized to learn the complex relationship between the target user and his/her neighbors and the complex relationship between the target item and its neighbors. Compared with DNNs, the shallow neural network, i.e., BP neural network, can not only reduce the computational and storage costs, but also alleviate the overfitting issues in DNNs caused by a relatively small number of ratings. In addition, an attention mechanism is designed to capture the global impact of the nearest users of the target user on their nearest target user sets. Extensive experiments conducted on eight benchmark datasets confirm the effectiveness of the proposed model.

To alleviate the sparsity issue, many recommender systems have been proposed to consider the review text as the auxiliary information to improve the recommendation quality. Despite success, they only use the ratings as the ground truth for error backpropagation. However, the rating information can only indicate the users' overall preference for the items, while the review text contains rich information about the users' preferences and the attributes of the items. In real life, reviews with the same rating may have completely opposite semantic information. If only the ratings are used for error backpropagation, the latent factors of these reviews will tend to be consistent, resulting in the loss of a large amount of review information. In this article, we propose a novel deep model termed deep rating and review neural network (DRRNN) for recommendation. Specifically, compared with the existing models that adopt the review text as the auxiliary information, DRRNN additionally considers both the target rating and target review of the given user-item pair as ground truth for error backpropagation in the training stage. Therefore, we can keep more semantic information of the reviews while making rating predictions. Extensive experiments on four publicly available datasets demonstrate the effectiveness of the proposed DRRNN model in terms of rating prediction.

Collaborative filtering (CF) is a classical model for recommender systems. Though neural network-based CF models have been shown to perform well in some cases, they still suffer from the sparsity and cold-start issues. To address these issues, we propose a novel neural network-based CF model, termed the cross-domain explicit–implicit-mixed CF neural network (CEICFNet). The proposed model utilizes deep neural networks to learn latent factors not only from the explicit ratings and the implicit interactions but also in a cross-domain manner. In particular, domain-shared multilayer perception (MLP) networks are designed to learn the user rating latent factors and the user interaction latent factors from the explicit ratings and the implicit interactions, respectively, which act as bridges for transferring knowledge across domains. On the other hand, domain-specific MLP networks are designed to learn the item rating latent factors and the item interaction latent factors from the explicit ratings and the implicit interactions, respectively. Then, in each domain, based on the user rating latent factors and the item rating latent factors, the rating predictive representation for each user–item pair can be learned by an MLP. Similarly, the interaction predictive representation for each user–item pair can be learned. For integrating the explicit ratings and the implicit interactions, in each domain, a fully connected layer is used to automatically assign different weights to the rating predictive representations and the interaction predictive representations, based on which the final interaction probability can be generated. Extensive experiments are conducted on five datasets and the results have confirmed the effectiveness of our model.

Community detection is a research hotspot in machine learning and data mining. However, most of the existing community detection methods only rely on the lower-order connectivity patterns, while ignoring the higher-order connectivity patterns, and unable to capture the building blocks of the complex network. In recent years, some community detection methods based on higher-order structures have been developed, but they mainly focus on the motif network composed of higher-order structures, which violate the original lower-order topological structure and are affected by the fragmentation issue, resulting in the deviation of community detection results. Therefore, there is still a lack of community detection methods that can effectively utilize higher-order connectivity patterns and lower-order connectivity patterns. To overcome the above limitations, this paper proposes the Hybrid-order Stochastic Block Model (HSBM) from the perspective of the generative model. Based on the classical stochastic block model, the generation of lower-order structure and higher-order structure of the network is modeled uniformly, and the original topological properties of the network are maintained while using higher-order connectivity patterns. At the same time, a heuristic algorithm for community detection is proposed to optimize the objective function. Extensive experiments on six real-world datasets show that the proposed method outperforms the existing approaches.

Many real-world graphs can be viewed as Heterogeneous Networks or Heterogeneous Information Networks (HINs) for that they comprise a diversity of node types and relation types. Due to the efficient representation ability of Graph Neural Network and the idea of random walk, many recent studies apply graph representation learning to HINs and achieve satisfactory results. However, these works either treat different node types in a metapath equally, which is inconsistent with the original graph semantic information for that different node types should have different statuses, or only consider the first-order (node-level) and second-order (metapath-level, a.k.a. link-level) information aggregation while ignoring the higher-order relations. To tackle these two problems, we propose a novel Star-Shaped Hierarchical Graph Attentional Network (StarGAT) model to boost representation learning in HINs. Specifically, we assume nodes in HINs can be categorized into a star-shaped structure including one center node type and a bunch of auxiliary node types in a specific task; and we encode node-level, link-level and motif-level attentions in a hierarchical manner to capture richer semantic information. Extensive experiments on three datasets illustrate the model effectiveness.

Most of the existing methods define recommendation as regression or classification for user-item interactions and apply discriminative models. However, recommender systems suffer from interaction data sparsity and data noise problems in reality. Recent Generative Adversarial Network-based recommender systems have the potential to solve the aforementioned problems. The negative sampling methods use the generator to collect effective signals from a large amount of unlabeled data to alleviate the data sparsity problem, while they suffer from sparse rewards in the policy gradient training process. The vector reconstruction methods generate user-related vectors for data augmentation to enhance robustness, but they lead to redundant calculation and only take the user as a condition and ignore information conveyed by items. To alleviate the limitations of these methods, we propose a novel framework termed Personalized Recommendation with Conditional Generative Adversarial Networks (PRGAN) to consider both the user and the item subset as conditions and formulate conditional rating vector generation as a user-item matching problem. The sparsity of conditional rating vectors can be controlled in our method, which simplifies the discriminator’s learning task. Experiments are conducted on four datasets to evaluate the effectiveness of the proposed framework.

Network embedding is an essential task in network analysis, which aims to learn low-dimensional representations for nodes in the networks. To obtain better embeddings, the structural information and inherent properties of networks should be preserved during the network embedding procedure. Recent studies only take into consideration the lower-order or microscopic structure of networks, such as the first- and second-order proximities between nodes. Most of them, however, fail to capture the mesoscopic structure and higher-order structure of networks which are also prominent features of networks, such as community structure and motifs (i.e. the frequently occurred small dense subgraph patterns of networks). In this paper, we propose a novel attention-based graph neural network called Multi-proximity Attention Network (MAN) for network embedding that simultaneously utilizes the underlying proximity dependencies between nodes from multiple aspects. Extensive experiments are conducted in two downstream tasks, namely node classification and node clustering. Experimental results demonstrate the effectiveness of the proposed method.

The aim of session-based recommendation is to predict the next-clicked item based on the anonymous behavior sequence. The existing works on session-based recommendation mainly capture the user preference within an individual session. This paper proposes a novel approach, called Session-based Recommendation with Heterogeneous Graph Neural Networks (SR-HGNN) to exploit cross-session information for better inferring the user preference of the current session. Specifically, we propose to use a heterogeneous graph to model the current session sequence and cross-session information simultaneously. After that, we come up with a novel model to pass messages along edges of different types hierarchically. Extensive experiments conducted on three real-world datasets demonstrate the superiority of SR-HGNN by comparing with different state-of-the-art baselines.

Many efforts have been made on developing multi-view network community detection approaches. However, most of them can only reveal non-overlapping community structure. In this paper, we propose a novel approach for Overlapping Community Detection in Multi-view Brain Network (oComm). For modeling the overlapping community structure, a community membership strength vector is introduced for each node in each view, based on which a network generative model is designed to measure the within-view community quality. For measuring the consistency of overlapping community structures across different views, the Jaccard similarity is adopted to measure the first-order structural consistency of one node across different views, based on which a cross-view community consistency model is established. One objective function is defined by integrating the above two components. By solving the objective function via the alternative coordinate gradient ascent method, the optimal community membership strength vectors are generated, from which the multi-view overlapping community structure is obtained. Additionally, this study collects a set of EEG data of 147 subjects from Department of Otolaryngology of Sun Yat-sen Memorial Hospital, Sun Yat-sen University, based on which three multi-view brain networks are constructed. Comparison results with several existing approaches have confirmed the effectiveness of the proposed method.

Recently, the proximity-based methods have achieved great success for multiview clustering. Nevertheless, most existing proximity-based methods take the predefined proximity matrices as input and their performance relies heavily on the quality of the predefined proximity matrices. A few multiview proximity learning (MVPL) methods have been proposed to tackle this problem but there are still some limitations, such as only emphasizing the intraview relation but overlooking the inter-view correlation, or not taking the weight differences of different views into account when considering the inter-view correlation. These limitations affect the quality of the learned proximity matrices and therefore influence the clustering performance. With the aim of breaking through these limitations simultaneously, a novel proximity learning method, called adaptively weighted MVPL (AWMVPL), is proposed. In the proposed method, both the intraview relation and the inter-view correlation are considered. Besides, when considering the inter-view correlation, the weights of different views are learned in a self-weighted scheme. Furthermore, through an adaptively weighted scheme, the information of the learned view-specific proximity matrices is integrated into a view-common cluster indicator matrix which outputs the final clustering result. Extensive experiments are conducted on several synthetic and real-world datasets to demonstrate the effectiveness and superiority of our method compared with the existing methods.

The city's district attraction ranking plays an essential role in the city's government because it can be used to reveal the city's district attraction and, thus, help government make decisions for urban planning in terms of the smart city. The traditional methods for urban planning mainly rely on the district's GDP, employment rate, population density, information from questionnaire surveys, and so on. However, as a comparison, such information is becoming relatively less informative as the explosion of an increasing amount of urban data. What is more, there is a serious shortcoming in these methods, i.e., they are independent representations of the attraction of a district and do not take into account the interaction among districts. With the development of urban computing, it is possible to make good use of urban data for urban planning. To this end, based on taxi big data obtained from Guangzhou, China, this article proposes a district attraction ranking approach called AttractRank, which for the first time uses taxi big data for district ranking. An application system is developed for demonstration purposes. First, the entire Guangzhou city is divided into a number of districts by using constrained K-means. Second, the original PageRank algorithm is extended to integrate with the taxi's origin-destination (OD) points to establish the OD matrix, whereby the attraction ranking of each district can be calculated. Finally, by visualizing the results and case studies obtained from AttractRank, we can successfully obtain the pattern of how attractions of districts change over time and interesting discoveries on urban lives; therefore, it has wide applications in urban planning and urban data mining.

As a critical task in intelligent traffic systems, traffic prediction has received a large amount of attention in the past few decades. The early efforts mainly model traffic prediction as the time-series mining problem, in which the spatial dependence has been largely ignored. As the rapid development of deep learning, some attempts have been made in modeling traffic prediction as the spatio-temporal data mining problem in a road network, in which deep learning techniques can be adopted for modeling the spatial and temporal dependencies simultaneously. Despite the success, the spatial and temporal dependencies are only modeled in a regionless network without considering the underlying hierarchical regional structure of the spatial nodes, which is an important structure naturally existing in the real-world road network. Apart from the challenge of modeling the spatial and temporal dependencies like the existing studies, the extra challenge caused by considering the hierarchical regional structure of the road network lies in simultaneously modeling the spatial and temporal dependencies between nodes and regions and the spatial and temporal dependencies between regions. To this end, this article proposes a new Temporal Hierarchical Graph Attention Network (TH-GAT). The main idea lies in augmenting the original road network into a region-augmented network, in which the hierarchical regional structure can be modeled. Based on the region-augmented network, the region-aware spatial dependence model and the region-aware temporal dependence model can be constructed, which are two main components of the proposed TH-GAT model. In addition, in the region-aware spatial dependence model, the graph attention network is adopted, in which the importance of a node to another node, of a node to a region, of a region to a node, and of a region to another region, can be captured automatically by means of the attention coefficients. Extensive experiments are conducted on two real-world traffic datasets, and the results have confirmed the superiority of the proposed TH-GAT model.

Recently, network embedding has received a large amount of attention in network analysis. Although some network embedding methods have been developed from different perspectives, on one hand, most of the existing methods only focus on leveraging the plain network structure, ignoring the abundant attribute information of nodes. On the other hand, for some methods integrating the attribute information, only the lower-order proximities (e.g., microscopic proximity structure) are taken into account, which may suffer if there exists the sparsity issue and the attribute information is noisy. To overcome this problem, the attribute information and mesoscopic community structure are utilized. In this article, we propose a novel network embedding method termed Attributed Network Embedding with Micro-Meso structure, which is capable of preserving both the attribute information and the structural information including the microscopic proximity structure and mesoscopic community structure. In particular, both the microscopic proximity structure and node attributes are factorized by Nonnegative Matrix Factorization (NMF), from which the low-dimensional node representations can be obtained. For the mesoscopic community structure, a community membership strength matrix is inferred by a generative model (i.e., BigCLAM) or modularity from the linkage structure, which is then factorized by NMF to obtain the low-dimensional node representations. The three components are jointly correlated by the low-dimensional node representations, from which two objective functions (i.e., ANEM_B and ANEM_M) can be defined. Two efficient alternating optimization schemes are proposed to solve the optimization problems. Extensive experiments have been conducted to confirm the superior performance of the proposed models over the state-of-the-art network embedding methods.

Multi-layer network community detection has drawn an increasing amount of attention recently. Despite success, the existing methods mainly focus on the lower-order connectivity structure at the level of individual nodes and edges. And the higher-order connectivity structure has been largely ignored, which contains better signature of community compared with edges. The main challenges in utilizing higher-order structure for multi-layer network community detection are that the most representative higher-order structure may vary from one layer to another and the connectivity structure formed by the same node subset may exhibit different higher-order connectivity patterns in different layers. To this end, this paper proposes a novel higher-order structure, termed harmonic motif, which is a dense subgraph having on average the largest statistical significance in each layer. Based on the harmonic motif, a primary layer is constructed by integrating higher-order structural information from all layers. Additionally, the higher-order structural information of each individual layer is taken as the auxiliary information. A coupling is established between the primary layer and each auxiliary layer. Accordingly, a harmonic motif modularity is designed to generate the community structure. Extensive experiments on eleven real-world multi-layer network datasets have been conducted to confirm the effectiveness of the proposed method.

Multiview subspace clustering has attracted an increasing amount of attention in recent years. However, most of the existing multiview subspace clustering methods assume linear relations between multiview data points when learning the affinity representation by means of the self-expression or fail to preserve the locality property of the original feature space in the learned affinity representation. To address the above issues, in this article, we propose a new multiview subspace clustering method termed smoothness regularized multiview subspace clustering with kernel learning (SMSCK). To capture the nonlinear relations between multiview data points, the proposed model maps the concatenated multiview observations into a high-dimensional kernel space, in which the linear relations reflect the nonlinear relations between multiview data points in the original space. In addition, to explicitly preserve the locality property of the original feature space in the learned affinity representation, the smoothness regularization is deployed in the subspace learning in the kernel space. Theoretical analysis has been provided to ensure that the optimal solution of the proposed model meets the grouping effect. The unique optimal solution of the proposed model can be obtained by an optimization strategy and the theoretical convergence analysis is also conducted. Extensive experiments are conducted on both image and document data sets, and the comparison results with state-of-the-art methods demonstrate the effectiveness of our method.

Ensemble clustering has been a popular research topic in data mining and machine learning. Despite its significant progress in recent years, there are still two challenging issues in the current ensemble clustering research. First, most of the existing algorithms tend to investigate the ensemble information at the object-level, yet often lack the ability to explore the rich information at higher levels of granularity. Second, they mostly focus on the direct connections (e.g., direct intersection or pair-wise co-occurrence) in the multiple base clusterings, but generally neglect the multiscale indirect relationship hidden in them. To address these two issues, this paper presents a novel ensemble clustering approach based on fast propagation of cluster-wise similarities via random walks. We first construct a cluster similarity graph with the base clusters treated as graph nodes and the cluster-wise Jaccard coefficient exploited to compute the initial edge weights. Upon the constructed graph, a transition probability matrix is defined, based on which the random walk process is conducted to propagate the graph structural information. Specifically, by investigating the propagating trajectories starting from different nodes, a new cluster-wise similarity matrix can be derived by considering the trajectory relationship. Then, the newly obtained cluster-wise similarity matrix is mapped from the cluster-level to the object-level to achieve an enhanced co-association matrix, which is able to simultaneously capture the object-wise co-occurrence relationship as well as the multiscale cluster-wise relationship in ensembles. Finally, two novel consensus functions are proposed to obtain the consensus clustering result. Extensive experiments on a variety of real-world datasets have demonstrated the effectiveness and efficiency of our approach.

The issue of impulse noise detection and reduction is a critical problem for image processing application systems. In order to detect impulse noises in corrupted images, a statistic named local consensus index (LCI) is proposed for quantitatively evaluating how noise free a pixel is, and then an impulse noise detection scheme based on LCI is introduced. First, the similarity between arbitrary two pixels in an image is quantified based on both their geometric distance and intensity difference, and the LCI of arbitrary pixel is calculated by summing all the similarity values of pixels in its neighborhood. As a new statistic, the value of LCI indicates the local consensus of the concerned pixel regarding its neighbors and could also tell whether a pixel is noise free or impulsive. Therefore, LCI can be directly used as an efficient indicator of impulse noise. Furthermore, to improve the performance of impulse noise detection, different strategies are applied to the pixels at flat regions and the ones with complex textures, since distributions of LCI value within those regions are totally different. As for impulse noise filtering, a hybrid graph Laplacian regularization (HGLR) method is introduced to restore the intensities of those pixels degraded by impulse noise. We conduct extensive experiments to verify the effectiveness of our impulsive noise detection and reduction method, and the results show that the proposed method outperforms the state-of-the-art techniques in terms of impulse detection and noise removal.

Previous multi-view clustering algorithms mostly partition the multi-view data in their original feature space, the efficacy of which heavily and implicitly relies on the quality of the original feature presentation. In light of this, this paper proposes a novel approach termed Multi-view Clustering in Latent Embedding Space (MCLES), which is able to cluster the multi-view data in a learned latent embedding space while simultaneously learning the global structure and the cluster indicator matrix in a unified optimization framework. Specifically, in our framework, a latent embedding representation is firstly discovered which can effectively exploit the complementary information from different views. The global structure learning is then performed based on the learned latent embedding representation. Further, the cluster indicator matrix can be acquired directly with the learned global structure. An alternating optimization scheme is introduced to solve the optimization problem. Extensive experiments conducted on several real-world multi-view datasets have demonstrated the superiority of our approach.

Graph Neural Networks (GNNs), which extend deep neural networks to graph-structured data, have attracted increasing attention. They have been proven to be powerful for numerous graph related tasks such as graph classification, link prediction, and node classification. To adapt GNNs to graph classification, recent works aim to learn graph-level representation through a hierarchical pooling procedure. One major direction is to select important nodes to hierarchically coarsen the input graph and gradually reduce the information into the graph representation. However, most of the existing methods only select important nodes, which can be redundant and cannot represent the original graph well. Meanwhile, the information of non-selected nodes is often overlooked when generating a new coarser graph, which may lead to the tremendous loss of important structural and node feature information. In this paper, we propose a novel pooling operator RepPool to learn hierarchical graph representations. Specifically, we introduce the concept of representativeness that is combined with the importance for node selection and we provide a learnable way to integrate non-selected nodes. By combining the RepPool operator with conventional GCN convolutional layers, a hierarchical graph classification architecture is developed. Extensive experiments on various public benchmarks have demonstrated the effectiveness of the proposed method. The implementation of the proposed framework is available.

Community detection (or graph clustering) is crucial for unraveling the structural properties of complex networks. As an important technique in community detection, label propagation has shown the advantage of finding a good community structure with nearly linear time complexity. However, despite the progress that has been made, there are still several important issues that have not been properly addressed. First, the label propagation typically proceeds over the lower order structure of the network and only the direct one-hop connections between nodes are taken into consideration. Unfortunately, the higher order structure that may encode design principle of the network and be crucial for community detection is neglected under this regime. Second, the stability of the identified community structure may also be seriously affected by the inherent randomness in the label propagation process. To tackle the above issues, this article proposes a Motif-Aware Weighted Label Propagation method for community detection. We focus on triangles within the network, but our technique extends to other kinds of motifs as well. Specifically, the motif-based higher order structure mining is conducted to capture structural characteristics of the network. First, the motif of interest (locally meaningful pattern) is identified, and then, the motif-based hypergraph can be constructed to encode the higher order connections. To further utilize the structural information of the network, a re-weighted network is designed, which unifies both the higher order structure and the original lower order structure. Accordingly, a novel voting strategy termed NaS (considering both Number and Strength of connections) is proposed to update node labels during the label propagation process. In this way, the random label selection can be effectively eliminated, yielding more stable community structures. Experimental results on multiple real-world datasets have shown the superiority of the proposed method.

This paper focuses on scalability and robustness of spectral clustering for extremely large-scale datasets with limited resources. Two novel algorithms are proposed, namely, ultra-scalable spectral clustering (U-SPEC) and ultra-scalable ensemble clustering (U-SENC). In U-SPEC, a hybrid representative selection strategy and a fast approximation method for K-nearest representatives are proposed for the construction of a sparse affinity sub-matrix. By interpreting the sparse sub-matrix as a bipartite graph, the transfer cut is then utilized to efficiently partition the graph and obtain the clustering result. In U-SENC, multiple U-SPEC clusterers are further integrated into an ensemble clustering framework to enhance the robustness of U-SPEC while maintaining high efficiency. Based on the ensemble generation via multiple U-SEPC's, a new bipartite graph is constructed between objects and base clusters and then efficiently partitioned to achieve the consensus clustering result. It is noteworthy that both U-SPEC and U-SENC have nearly linear time and space complexity, and are capable of robustly and efficiently partitioning 10-million-level nonlinearly-separable datasets on a PC with 64 GB memory. Experiments on various large-scale datasets have demonstrated the scalability and robustness of our algorithms. The MATLAB code and experimental data are available at https://www.researchgate.net/publication/330760669.

This article studies a new problem of data stream clustering, namely, multiview data stream (MVStream) clustering. Although many data stream clustering algorithms have been developed, they are restricted to the single-view streaming data, and clustering MVStreams still remains largely unsolved. In addition to the many issues encountered by the conventional single-view data stream clustering, such as capturing cluster evolution and discovering clusters of arbitrary shapes under the limited computational resources, the main challenge of MVStream clustering lies in integrating information from multiple views in a streaming manner and abstracting summary statistics from the integrated features simultaneously. In this article, we propose a novel MVStream clustering algorithm for the first time. The main idea is to design a multiview support vector domain description (MVSVDD) model, by which the information from multiple insufficient views can be integrated, and the outputting support vectors (SVs) are utilized to abstract the summary statistics of the historical multiview data objects. Based on the MVSVDD model, a new multiview cluster labeling method is designed, whereby clusters of arbitrary shapes can be discovered for each view. By tracking the cluster labels of SVs in each view, the cluster evolution associated with concept drift can be captured. Since the SVs occupy only a small portion of data objects, the proposed MVStream algorithm is quite efficient with the limited computational resources. Extensive experiments are conducted to demonstrate the effectiveness and efficiency of the proposed method.

In general, recommendation can be viewed as a matching problem, i.e., match proper items for proper users. However, due to the huge semantic gap between users and items, it’s almost impossible to directly match users and items in their initial representation spaces. To solve this problem, many methods have been studied, which can be generally categorized into two types, i.e., representation learning-based CF methods and matching function learning-based CF methods. Representation learning-based CF methods try to map users and items into a common representation space. In this case, the higher similarity between a user and an item in that space implies they match better. Matching function learning-based CF methods try to directly learn the complex matching function that maps user-item pairs to matching scores. Although both methods are well developed, they suffer from two fundamental flaws, i.e., the limited expressiveness of dot product and the weakness in capturing low-rank relations respectively. To this end, we propose a general framework named DeepCF, short for Deep Collaborative Filtering, to combine the strengths of the two types of methods and overcome such flaws. Extensive experiments on four publicly available datasets demonstrate the effectiveness of the proposed DeepCF framework.

In this paper, we define a new problem of multi-layer network community detection, namely higher-order multi-layer community detection. A multi-layer motif (M-Motif) approach is proposed, which discovers communities with good intralayer higher-order community quality while preserving interlayer higher-order community consistency. Experimental results have confirmed the superiority of the proposed method.

This paper studies the problem of generalized zero-shot learning which requires the model to train on image-label pairs from some seen classes and test on the task of classifying new images from both seen and unseen classes. In this paper, we propose a novel model that provides a unified framework for three different approaches: visual → semantic mapping, semantic → visual mapping, and metric learning. Specifically, our proposed model consists of a feature generator that can generate various visual features given class embeddings as input, a regressor that maps each visual feature back to its corresponding class embedding, and a discriminator that learns to evaluate the closeness of an image feature and a class embedding. All three components are trained under the combination of cyclic consistency loss and dual adversarial loss. Experimental results show that our model not only preserves higher accuracy in classifying images from seen classes, but also performs better than existing state-of-the-art models in in classifying images from unseen classes.

Recently, multi-view outlier detection has emerged as a challenging research topic in outlier detection because of complex distributions of data across different views. There are mainly three types of outliers, i.e., attribute outliers, class outliers and class-attribute outliers. Most existing multi-view outlier detection approaches only detect part of the three types of outliers in a pairwise manner across different views, which is not able to accomplish the task of multi-view outlier detection comprehensively and uniformly. Outlier detection in a pairwise manner across different views also leads to time-consuming computation. We propose a new algorithm termed Multi-view Outlier Detection in Deep Intact Space (MODDIS) to find all the three types of outliers simultaneously and avoid comparing different views in a pairwise manner. Rather than leveraging subspace clustering, the performance of which is seriously affected by the dependence of subspaces on most real datasets, neural networks are employed in MODDIS in that neural networks have a stronger representation learning ability. Meanwhile, based on the view insufficiency assumption, a multi-view intact outlierness space assumption is proposed. Based on this assumption, a multi-view latent intact space is constructed to encode outlierness information of all views, where outlierness in any view is a snapshot from some perspective. Finally, an outlier detection measurement is defined in the latent intact space. Experiments are conducted on several UCI datasets and the empirical results demonstrate the effectiveness of our proposed method.

Graph Learning has emerged as a promising technique for multi-view clustering, and has recently attracted lots of attention due to its capability of adaptively learning a unified and probably better graph from multiple views. However, the existing multi-view graph learning methods mostly focus on the multi-view consistency, but neglect the potential multi-view inconsistency (which may be incurred by noise, corruptions, or view-specific characteristics). To address this, this paper presents a new graph learning-based multi-view clustering approach, which for the first time, to our knowledge, simultaneously and explicitly formulates the multi-view consistency and the multi-view inconsistency in a unified optimization model. To solve this model, a new alternating optimization scheme is designed, where the consistent and inconsistent parts of each single-view graph as well as the unified graph that fuses the consistent parts of all views can be iteratively learned. It is noteworthy that our multi-view graph learning model is applicable to both similarity graphs and dissimilarity graphs, leading to two graph fusion-based variants, namely, distance (dissimilarity) graph fusion and similarity graph fusion. Experiments on various multi-view datasets demonstrate the superiority of our approach. The MATLAB source code is available at https://github.com/youweiliang/ConsistentGraphLearning.

Recommender system is widely used in e-commercial platforms to recommend users suitable items according to users's preferences. In recent years, an increasing amount of attention has been paid to the application of recommender system in education. There are many online learning systems that can recommend students suitable courses according to students' learning performances. However, there are few universities using recommender system to recommend students suitable elective courses. It is generally known that students in higher grade take the courses earlier than those in lower grade. Therefore, the elective course scores of sophomores can be predicted by using the course score information from students of higher grades. However, the unbalanced distribution of course-enrollment data makes it hard to predict the scores of the courses that are in a low selection rate. Therefore, we propose a Constrained Matrix Factorization (ConMF) algorithm to predict sophomores' elective course scores, which integrates the course average score into the objective function so as to make up the prediction deviation caused by the unbalanced course selection rate and make more accurate prediction than the traditional Matrix Factorization (MF) approach. The experimental results show that our proposed model outperforms the state-of-the-art methods in the task of university students' course score prediction.

Inspired by the significant success of deep learning, some attempts have been made to introduce deep neural networks (DNNs) in recommendation systems to learn users' preferences for items. Since DNNs are well suitable for representation learning, they enable recommendation systems to generate more accurate prediction. However, they inevitably result in high computational and storage costs. Worse still, due to the relatively small number of ratings that can be fed into DNNs, they may easily lead to over-fitting. To tackle these problems, we propose a novel recommendation algorithm based on Back Propagation (BP) neural network with Attention Mechanism (BPAM). In particular, the BP neural network is utilized to learn the complex relationship of the target users and their neighbors. Compared with deep neural network, the shallow neural network, i.e., BP neural network, can not only reduce the computational and storage costs, but also prevent the model from over-fitting. In addition, an attention mechanism is designed to capture the global impact on all nearest target users for each user. Extensive experiments on eight benchmark datasets have been conducted to evaluate the effectiveness of the proposed model.

Network community detection is a hot research topic in network analysis. Although many methods have been proposed for community detection, most of them only take into consideration the lower-order structure of the network at the level of individual nodes and edges. Thus, they fail to capture the higher-order characteristics at the level of small dense subgraph patterns, e.g., motifs. Recently, some higher-order methods have been developed but they typically focus on the motif-based hypergraph which is assumed to be a connected graph. However, such assumption cannot be ensured in some real-world networks. In particular, the hypergraph may become fragmented. That is, it may consist of a large number of connected components and isolated nodes, despite the fact that the original network is a connected graph. Therefore, the existing higher-order methods would suffer seriously from the above fragmentation issue, since in these approaches, nodes without connection in hypergraph can't be grouped together even if they belong to the same community. To address the above fragmentation issue, we propose an Edge enhancement approach for Motif-aware community detection (EdMot ). The main idea is as follows. Firstly, a motif-based hypergraph is constructed and the top K largest connected components in the hypergraph are partitioned into modules. Afterwards, the connectivity structure within each module is strengthened by constructing an edge set to derive a clique from each module. Based on the new edge set, the original connectivity structure of the input network is enhanced to generate a rewired network, whereby the motif-based higher-order structure is leveraged and the hypergraph fragmentation issue is well addressed. Finally, the rewired network is partitioned to obtain the higher-order community structure. Extensive experiments have been conducted on eight real-world datasets and the results show the effectiveness of the proposed method in improving the community detection performance of state-of-the-art methods.

Collaborative filtering (CF) algorithms have been widely used to build recommender systems since they have distinguishing capability of sharing collective wisdoms and experiences. However, they may easily fall into the trap of the Matthew effect, which tends to recommend popular items and hence less popular items become increasingly less popular. Under this circumstance, most of the items in the recommendation list are already familiar to users and therefore the performance would seriously degenerate in finding cold items, i.e., new items and niche items. To address this issue, in this paper, a user survey is first conducted on the online shopping habits in China, based on which a novel recommendation algorithm termed innovator-based CF is proposed that can recommend cold items to users by introducing the concept of innovators. Specifically, innovators are a special subset of users who can discover cold items without the help of recommender system. Therefore, cold items can be captured in the recommendation list via innovators, achieving the balance between serendipity and accuracy. To confirm the effectiveness of our algorithm, extensive experiments are conducted on the dataset provided by Alibaba Group in Ali Mobile Recommendation Algorithm Competition, which is collected from the real e-commerce environment and covers massive user behavior log data.

Mood disorders are common and associated with significant morbidity and mortality. Early diagnosis has the potential to greatly alleviate the burden of mental illness and the ever increasing costs to families and society. Mobile devices provide us a promising opportunity to detect the users' mood in an unobtrusive manner. In this study, we use a custom keyboard which collects keystrokes' meta-data and accelerometer values. Based on the collected time series data in multiple modalities, we propose a deep personalized mood prediction approach, called dpMood, by integrating convolutional and recurrent deep architectures as well as exploring each individual's circadian rhythm. Experimental results not only demonstrate the feasibility and effectiveness of using smart-phone meta-data to predict the presence and severity of mood disturbances in bipolar subjects, but also show the potential of personalized medical treatment for mood disorders.

During the past several years, multi-layer network community detection has drawn an increasing amount of attention and many approaches have been developed from different perspectives. Despite the success, they mainly rely on the lower-order connectivity structure at the level of individual nodes and edges. However, the higher-order connectivity structure plays the essential role as the building block for multiplex networks, which may contain better signature of community than edge. The main challenge in utilizing higher-order structure for multi-layer network community detection is that the most representative higher-order structure may vary from one layer to another. In this paper, we propose a higher-order structural approach for multi-layer network community detection, termed harmonic motif modularity (HM-Modularity). The key idea is to design a novel higher-order structure, termed harmonic motif, which is able to integrate higher-order structural information from multiple layers to construct a primary layer. The higher-order structural information of each individual layer is also extracted, which is taken as the auxiliary information for discovering the multi-layer community structure. A coupling is established between the primary layer and each auxiliary layer. Finally, a harmonic motif modularity is designed to generate the community structure. By solving the optimization problem of the harmonic motif modularity, the community labels of the primary layer can be obtained to reveal the community structure of the original multi-layer network. Experiments have been conducted to show the effectiveness of the proposed method.

Due to its ability to combine multiple base clusterings into a probably better and more robust clustering, the ensemble clustering technique has been attracting increasing attention in recent years. Despite the significant success, one limitation to most of the existing ensemble clustering methods is that they generally treat all base clusterings equally regardless of their reliability, which makes them vulnerable to low-quality base clusterings. Although some efforts have been made to (globally) evaluate and weight the base clusterings, yet these methods tend to view each base clustering as an individual and neglect the local diversity of clusters inside the same base clustering. It remains an open problem how to evaluate the reliability of clusters and exploit the local diversity in the ensemble to enhance the consensus performance, especially, in the case when there is no access to data features or specific assumptions on data distribution. To address this, in this paper, we propose a novel ensemble clustering approach based on ensemble-driven cluster uncertainty estimation and local weighting strategy. In particular, the uncertainty of each cluster is estimated by considering the cluster labels in the entire ensemble via an entropic criterion. A novel ensemble-driven cluster validity measure is introduced, and a locally weighted co-association matrix is presented to serve as a summary for the ensemble of diverse clusters. With the local diversity in ensembles exploited, two novel consensus functions are further proposed. Extensive experiments on a variety of real-world datasets demonstrate the superiority of the proposed approach over the state-of-the-art.

Missing value is common in many machine learning problems and much effort has been made to handle missing data to improve the performance of the learned model. Sometimes, our task is not to train a model using those unlabeled/labeled data with missing value but process examples according to the values of some specified features. So, there is an urgent need of developing a method to predict those missing values. In this paper, we focus on learning from the known values to learn missing value as close as possible to the true one. It's difficult for us to predict missing value because we do not know the structure of the data matrix and some missing values may relate to some other missing values. We solve the problem by recovering the complete data matrix under the three reasonable constraints: feature relationship, upper recovery error bound and class relationship. The proposed algorithm can deal with both unlabeled and labeled data and generative adversarial idea will be used in labeled data to transfer knowledge. Extensive experiments have been conducted to show the effectiveness of the proposed algorithms.

In recent years, combining multiple attributes of data sets including numerical and categorical attributes for data stream clustering has been a popular practice for improving clustering accuracy. In this paper, we propose a novel data stream clustering algorithm called WKStream which can cluster numerical and categorical data stream with different shapes efficiently. As different attributes have different significances in clustering process, different weights should be assigned for them. Accordingly, we design a new concept called Nonuniformity to assign weight to numerical attributes; While for the categorical attribute, we design a novel method to measure the distance between two values in the same categorical attribute. To discover clusters of different shapes, the concepts of micro-cluster and macro-cluster are utilized and extended to model complex cluster structures. Experimental results conducted on numerical, categorical and mixture real-world data sets have validated the effectiveness of the proposed method.

As a popular topic model, Probabilistic Latent Semantic Analysis (PLSA) has been widely applied in text clustering due to its reliability and practicability. While independence assumption contributes to its practicability, it loses the rich local information between words, which in some cases will result in incoherent topics. In this paper, we propose an enhanced PLSA model embedded with word correlation (EWPLSA) for text clustering. The new model can utilize a wide range of pairwise semantic constraints between words to get more meaningful topics. We study how to consider context information of all the words, trained with the global semantic distribution in a global manner. The relationship between similar words will be amplified in a global perspective in the next iteration. However, it will also be restricted by other words because all word contexts are considered. In this way, instead of generating independently, the topic assignment of each word is affected by other correlative words. In addition, we initialize several important parameters before the first iteration of the EM algorithm according to the local correlation information between words, leading to the reduction of iteration number. Experiments on two datasets have demonstrated the effectiveness of our method.

Expert finding is an important technique to obtain the user authority ranking in community question answering (CQA) websites. ZhihuRank is a topic-sensitive expert finding algorithm, which is based on both LDA and PageRank. Currently, with the amount of participants and documents increasing rapidly in CQA websites, how to parallel expert finding algorithms for big data analysis has received significant attention. In this paper, we find that the Spark framework is more suitable for paralleling expert finding algorithms than the MapReduce framework, which is a memory-based parallel computing model to support complicated iterative algorithms. As an example, we parallel ZhihuRank using MLlib's LDA and GraphX's PageRank in Spark. Experiments have been conducted on large-scale real data from Zhihu1(the most popular CQA website in China). And the experimental results confirmed the effectiveness and scalability of our proposed approach.

Point of interest (POI) recommendation, a service which can help people discover useful and interesting locations has emerged rapidly with the development of location-based social networks (LBSNs), like Foursquare, Gowalla and Wechat. The large number of check-in histories make it possible to mine the preference of each user and then to provide accurate personalized POI recommendation. In real-world applications, apart from check-in data, there are some other useful information available for making better POI recommendation, such as social relationship among users and geographical influence. In this paper, a new POI recommendation method called Social and Geographical Fusing Model (SGFM) is designed. The basic idea is summarized as follows. Firstly, the users' check-in records and social influence are integrated in a combinative model. Then the global user impact factors generated by the PageRank algorithm are used to improve the combinative model. Secondly, a geographical influence measurement is used to capture the users' physical check-in characters. Finally, the enhanced combinative model and geographical influence are combined together to form a new framework. Extensive experiments have been conducted on a famous dataset, namely Gowalla. The comparison results confirm that the proposed framework outperforms state-of-the-art POI recommendation methods significantly.

Support vector clustering (SVC) is a versatile clustering technique that is able to identify clusters of arbitrary shapes by exploiting the kernel trick. However, one hurdle that restricts the application of SVC lies in its sensitivity to the kernel parameter and the trade-off parameter. Although many extensions of SVC have been developed, to the best of our knowledge, there is still no algorithm that is able to effectively estimate the two crucial parameters in SVC without supervision. In this paper, we propose a novel support vector clustering approach termed ensemble-driven support vector clustering (EDSVC), which for the first time tackles the automatic parameter estimation problem for SVC based on ensemble learning, and is capable of producing robust clustering results in a purely unsupervised manner. Experimental results on multiple real-world datasets demonstrate the effectiveness of our approach.

Modularity is widely used in community detection in network. Despite its great success, one drawback is that its optimization is an NP-complete problem. To this end, this paper designs a new optimization method termed Global-Local-Search (Glo-Loc-Search for short). The basic idea lies in the two new important properties we have discovered which have seldom been noticed in the previous studies. The first one is that we can reformulate the modularity optimization problem into a bipartite graph-related optimization problem, and the other is the relationship transitivity among different but related node pairs. By making full use of those two properties and the transformation of some formulas, the proposed Glo-Loc-Search algorithm is designed, which is a combination of both global search and local search, with the computational complexity of O(n2). In the experiments, we have confirmed the effectiveness of our algorithm in generating high quality community structure with large modularity value. Additionally, the experiments demonstrate a supreme advantage compared with the existing methods in partitioning networks with some special structure.

It is well-known that recommendation system which is widely used in many e-commerce platforms to recommend items to the right users suffers from data sparsity, imbalanced rating and cold start problems. Matrix factorization is a good way to deal with the sparsity and imbalance problems, which is however unable to make prediction for new users due to the lack of auxiliary information. With the advent of online social networks, the trust relation in the social network can be utilized as auxiliary data to solve the aforementioned problems since consumers' buying behavior is usually affected by the people around them. This paper reports a study of exploiting the trust relationship in social network for personalized recommendation. Although previous studies have paid attention to this topic, we improve the quality of recommendation further and solve the cold start problem better. To this end, we propose a recommendation system in social network with Feature Transfer and Probabilistic Matrix Factorization (FTMF). The auxiliary data and matrix factorization technique are integrated to learn a social latent feature vector of users which represents the features transferred from trusted people. And an adaptive firm factor is introduced to balance the impact from user's own factors and trusted people on buying behavior for each user. The experimental results show that our model can effectively use the auxiliary data and outperforms the existing state-of-the-art social network based recommendation algorithms.

Robust online visual tracking is a challenging task of the computer vision due to its violent variation within the video sequences. To approach these issues, deep networks have been applied in order to improve accuracy and correlation filter based trackers perform excellent efficiency and adaptation to scale. In this paper, we present a novel method with convolutional networks and correlation filter. A simple two-layer convolutional network is constructed to learn robust representations, which encode the inner geometric layout and local structural information, and the tracking framework resorts to learning discriminative correlation filters based on them. For our method satisfies both veracity and efficiency by finding a compromise between two theories, it performs favorably against several state-of-the-art methods with 50 public challenging videos.

Although many successful ensemble clustering approaches have been developed in recent years, there are still two limitations to most of the existing approaches. First, they mostly overlook the issue of uncertain links, which may mislead the overall consensus process. Second, they generally lack the ability to incorporate global information to refine the local links. To address these two limitations, in this paper, we propose a novel ensemble clustering approach based on sparse graph representation and probability trajectory analysis. In particular, we present the elite neighbor selection strategy to identify the uncertain links by locally adaptive thresholds and build a sparse graph with a small number of probably reliable links. We argue that a small number of probably reliable links can lead to significantly better consensus results than using all graph links regardless of their reliability. The random walk process driven by a new transition probability matrix is utilized to explore the global information in the graph. We derive a novel and dense similarity measure from the sparse graph by analyzing the probability trajectories of the random walkers, based on which two consensus functions are further proposed. Experimental results on multiple real-world datasets demonstrate the effectiveness and efficiency of our approach.

The availability of many heterogeneous but related views of data has arisen in numerous clustering problems. Different views encode distinct representations of the same data, which often admit the same underlying cluster structure. The goal of multi-view clustering is to properly combine information from multiple views so as to generate high quality clustering results that are consistent across different views. Based on max-product belief propagation, we propose a novel multi-view clustering algorithm termed multi-view affinity propagation (MVAP). The basic idea is to establish a multi-view clustering model consisting of two components, which measure the within-view clustering quality and the explicit clustering consistency across different views, respectively. Solving this model is NP-hard, and a multi-view affinity propagation is proposed, which works by passing messages both within individual views and across different views. However, the exemplar consistency constraint makes the optimization almost impossible. To this end, by using some previously designed mathematical techniques, the messages as well as the cluster assignment vector computations are simplified to get simple yet functionally equivalent computations. Experimental results on several real-world multi-view datasets show that MVAP outperforms existing multi-view clustering algorithms. It is especially suitable for clustering more than two views.

In the past few years, instant messaging (IM) has been widely used in daily communication. However, due to the dispersion of topics and meaningless chatting, online IM groups are filled with useless messages. In order to help IM users capture what the IM group is talking about without reading all the messages, topic discovery in instant messages becomes a significant but challenging research task. In this paper, we propose a new method for topic detection in instant messages, which is applicable for the case where 1) useless terms keep emerging, 2) the instant messages are very short, and 3) multiple languages are used. The basic step is to treat each message in an online group discussion as a data item in message stream, and then apply PLSA on the collected instant messages. One strategy is designed to segment multilingual message without utilizing machine translation and remove the useless words that keep emerging. Extensive experiments conducted on the real world QQ group data confirm the effectiveness of the proposed method.

Recently, discovering dynamic communities has become an increasingly important task. Many algorithms have been proposed, most of which only use linkage structure. However, rich information is encoded in the content of social networks such as node content and edge content, which is essential to discover topically meaningful communities. Therefore, to detect both structurally and topically meaningful communities, linkage structure, node content and edge content should be integrated. The main challenge lies in how to integrate them dynamically in a seamless way. This paper proposes a novel transformation of content-based network into a Node-Edge Interaction (NEI) network where linkage structure, node content and edge content are embedded seamlessly. A differential activity based approach is proposed to incrementally maintain the NEI network as the content-based network evolves. To capture the semantic effect of different edge types, a transition probability matrix is devised for the NEI network. Based on this, heterogeneous random walk is applied to discover dynamic communities, leading to a new dynamic community detection method termed NEIWalk (NEI network based random Walk). Theoretical analysis shows that the proposed NEIWalk method gets a bounded accuracy loss due to the random walk sampling. Experimental results demonstrate the effectiveness and efficiency of NEIWalk.

In this paper, we aim to tackle the problem of discovering dynamic communities in weighted graph streams, especially when the underlying social behavior of individuals varies considerably over different graph regions. To tackle this problem, a novel structure termed Local Weighted-Edge-based Pattern (LWEP) Summary is proposed to describe a local homogeneous region. To efficiently compute LWEPs, some statistics need to be maintained according to the principle of preserving maximum weighted neighbor information with limited memory storage. To this end, the proposed approach is divided into online and offline components. During the online phase, we introduce some statistics, termed top-k neighbor lists and top-k candidate lists, to track. The key is to maintain only the top-k neighbors with the largest link weights for each node. To allow for less active neighbors to transition into top-k neighbors, an auxiliary data structure termed top-k candidate list is used to identify emerging active neighbors. The statistics can be efficiently maintained in the online component. In the offline component, these statistics are used at each snapshot to efficiently compute LWEPs. Clustering is then performed to consolidate LWEPs into high level clusters. Finally, mapping is made between clusters of consecutive snapshots to generate temporally smooth communities. Experimental results are presented to illustrate the effectiveness and efficiency of the proposed approach.

The affinity propagation (AP) clustering algorithm has received much attention in the past few years. AP is appealing because it is efficient, insensitive to initialization, and it produces clusters at a lower error rate than other exemplar-based methods. However, its single-exemplar model becomes inadequate when applied to model multisubclasses in some situations such as scene analysis and character recognition. To remedy this deficiency, we have extended the single-exemplar model to a multi-exemplar one to create a new multi-exemplar affinity propagation (MEAP) algorithm. This new model automatically determines the number of exemplars in each cluster associated with a super exemplar to approximate the subclasses in the category. Solving the model is NP-hard and we tackle it with the max-sum belief propagation to produce neighborhood maximum clusters, with no need to specify beforehand the number of clusters, multi-exemplars, and superexemplars. Also, utilizing the sparsity in the data, we are able to reduce substantially the computational time and storage. Experimental studies have shown MEAP's significant improvements over other algorithms on unsupervised image categorization and the clustering of handwritten digits.

In this paper, we propose a novel data stream clustering algorithm, termed SVStream, which is based on support vector domain description and support vector clustering. In the proposed algorithm, the data elements of a stream are mapped into a kernel space, and the support vectors are used as the summary information of the historical elements to construct cluster boundaries of arbitrary shape. To adapt to both dramatic and gradual changes, multiple spheres are dynamically maintained, each describing the corresponding data domain presented in the data stream. By allowing for bounded support vectors (BSVs), the proposed SVStream algorithm is capable of identifying overlapping clusters. A BSV decaying mechanism is designed to automatically detect and remove outliers (noise). We perform experiments over synthetic and real data streams, with the overlapping, evolving, and noise situations taken into consideration. Comparison results with state-of-the-art data stream clustering methods demonstrate the effectiveness and efficiency of the proposed method.

Partitioning nonlinearly separable datasets is a basic problem that is associated with data clustering. In this paper, a novel approach that is termed graph-based multiprototype competitive learning (GMPCL) is proposed to handle this problem. A graph-based method is employed to produce an initial, coarse clustering. After that, a multiprototype competitive learning is introduced to refine the coarse clustering and discover clusters of an arbitrary shape. The GMPCL algorithm is further extended to deal with high-dimensional data clustering, i.e., the fast graph-based multiprototype competitive learning (FGMPCL) algorithm. An experimental comparison has been performed by the exploitation of both synthetic and real-world datasets to validate the effectiveness of the proposed methods. Additionally, we apply our GMPCL/FGMPCL to two computer-vision tasks, namely, automatic color image segmentation and video clustering. Experimental results show that GMPCL/FGMPCL provide an effective and efficient tool with application to computer vision.

Kernel-based clustering is one of the most popular methods for partitioning nonlinearly separable dataset. However, exhaustive search for the global optimum is NP-hard. Iterative procedure such as k-means can be used to seek one of the local minima. Unfortunately, it is easily trapped into degenerate local minima when the prototypes of clusters are ill-initialized. In this paper, we restate the optimization problem of kernel-based clustering in an on-line learning framework, whereby a conscience mechanism is easily integrated to tackle the ill-initialization problem and faster convergence rate is achieved. Thus, we propose a novel approach termed conscience on-line learning (COLL). For each randomly taken data point, our method selects the winning prototype based on the conscience mechanism to bias the ill-initialized prototype to avoid degenerate local minima, and efficiently updates the winner by the on-line learning rule. Therefore, it can more efficiently obtain smaller distortion error than k-means with the same initialization. Experimental results on synthetic and large-scale real-world datasets, as well as that in the application of video clustering, have demonstrated the significant improvement over existing kernel clustering methods.

Fisher's Linear Discriminant Analysis (FLDA) uses the parametric form of the scatter matrix which is based on the Gaussian distribution assumption, and requires the scatter matrices to be nonsingular, which can not always be satisfied. To overcome this problem, many scholars have recently proposed Non-parametric Discriminant Analysis (NPDA), addressing the non-Gaussian aspects of sample distributions. In this paper, from the nearest neighborhood perspective, a new formulation of scatter matrices is presented to improve the NPDA, simultaneously emphasizing the boundary information and local structure contained in the training set. Then, CS decomposition is incorporated to improve its performance. Experimental results on 4 databases demonstrate the effectiveness of the improved method.