Graph Neural Networks (GNNs) have demonstrated impressive performance across diverse graph-based tasks by leveraging message passing to capture complex node relationships. However, on large-scale real-world graphs, GNNs face two major challenges: (1) GNNs struggle to ensure scalability and efficiency as repeated aggregation of large neighborhoods incurs significant computational overhead; (2) GNNs suffer from over-smoothing, where excessive propagation makes node representations indistinguishable, hindering model expressiveness. To tackle these, we propose ScaleGNN, which adaptively fuses multi-hop node features for scalable and effective graph learning. We first compute per-hop pure-neighbor matrices to isolate exclusive structural signals, then apply lightweight fusion to balance low- and high-order information, preserving both local detail and global correlations. To curb redundancy and over-smoothing, we introduce Local Contribution Score (LCS)–based masking to prune low-relevance high-order neighbors, and impose learnable sparsity to selectively integrate valuable multi-hop features. Extensive experiments on real-world datasets show that ScaleGNN consistently outperforms state-of-the-art GNNs in both predictive accuracy and computational efficiency. The source code is available at https://github.com/lx970414/ScaleGNN.

Out-of-Distribution (OOD) detection, which seeks to identify samples deviating from the In-Distribution (ID) training distribution at test time, is crucial for building robust machine learning systems. While extensive efforts have been made for Euclidean data, OOD detection on graph-structured data remains relatively underexplored. On the one hand, the specific properties of a graph may be attributed to its substructures. On the other hand, acquiring labeled data for graph learning is typically time-consuming and labor-intensive. Toward this end, in this paper, we propose a novel kernel-enhanced graph substructure learning framework termed KEGOD for unsupervised graph OOD detection. Specifically, we introduce a learnable graph generator to construct the augmented graph view that preserves distinguishable structure information. Then, for both the input graph and augmented view, a graph neural network (GNN) branch and a graph kernel (GK) branch are incorporated to explore graph latent patterns. By performing multi-branch concordance learning on the extracted graph patterns, our KEGOD captures complementary ID structural semantics in both implicit and explicit manners, enabling reliable detection of OOD graphs through semantic inconsistency. Finally, we build a self-adaptive training mechanism to automatically control diverse sensitivities of the graph patterns. Experimental results on several public graph datasets reveal the superiority of our KEGOD. Our code is available at~ https://github.com/jamesyifan/KEGOD.

Federated learning is a promising bridge that connects machine learning methods and multi-central medical data. It trains models using the local data, and protects the privacy of data. There are many methods for federated learning to aggregate models, especially personalized methods, which show relatively excellent performance. However, most of them excessively pay attention to global and local information while ignoring the random components during aggregating. That limits their performance in metrics like accuracy, specificity, and sensitivity. We propose a method (denoted by FedDiv) to make a balance between these metrics. The basic idea is to extract centralized features meanwhile filtering random components, and conduct personalized aggregation. These centralized features draw encoders’ attention, which enhances the performance of personalized models in specificity and sensitivity. Besides, they contain more global and local information, which is advantageous for personalized aggregation. Meanwhile, our personalized method preserves the local information as far as possible during aggregating models. These local information are the critical factor for the personalized models to perform better in accuracy. Finally, we validate this method in three public and one private medical datasets. Comparing with 14 federated methods, our method achieves the best performance in metrics including accuracy, specificity, sensitivity, and F1 score.

Multi-view clustering (MVC) leverages complementary information across heterogeneous views to improve unsupervised partitioning. Nevertheless, the majority of existing MVC methods critically assume that samples are fully aligned across views, an assumption frequently violated in practice when multi-view data are collected from independent sources without any correspondence. This gives rise to Completely Unaligned multi-view Clustering (CUC), where cross-view sample correspondences are entirely unknown, fundamentally impeding effective multi-view fusion. Prior CUC-oriented methods typically infer inter-view relations from distance/similarity matrices; however, severe cross-view heterogeneity often induces over-smoothing in such matrices, leading to unreliable matching signals and degraded clustering performance. To address these issues, we propose Cross-view Graph Matching for Completely Unaligned multi-view Clustering (CGM-CUC), a unified framework that couples structure-aware representation learning with progressive cross-view alignment. Specifically, CGM-CUC introduces a bipartite graph-based sample re-encoding mechanism to enhance discriminative structural cues, and an iterative cross-view matching network that progressively refines permutation matrices to recover latent correspondences. Moreover, we develop an alignment-guided optimization strategy that mitigates the over-smoothing effect in similarity estimation, thereby stabilizing the matching process and improving downstream clustering. Extensive experiments on multiple benchmark datasets demonstrate that CGM-CUC consistently achieves superior performance over state-of-the-art baselines, with particularly notable gains under fully unaligned view settings.

Multimodal clustering (MMC) overcomes the limitations of unimodal methods by integrating information from multiple sources, but the complexity of heterogeneous information coupling hinders effective feature extraction. Critically, existing MMC paradigms primarily focus on capturing consensus through coarse-grained cross-modal alignment. However, such task-agnostic strategies overlook the differences in the utility of feature information across varying task environments. In the absence of task-centric guidance, models often struggle to effectively distinguish task-relevant critical information from task-irrelevant redundant noise during the disentanglement process, leading to information confusion in the representation space. To address this challenge, we propose a deep disentangled multimodal clustering method guided by information theory, named DRLMMC, which employs a tripartite information optimization mechanism to achieve deep disentanglement of cross-modal representations. 1) We design modality-specific encoders to construct nonlinear mapping spaces, transforming the reconstruction mechanism of autoencoders into an information-theoretic mutual information (MI) constraint problem, preserving the unique features of different modalities; 2) To establish cross-modal semantic associations, it constructs a cross-modal shared information extraction module, and, based on an information-theoretic framework, designs an optimization objective function to progressively align multimodal feature subspaces through MI maximization and contrastive learning, capturing task-relevant invariant features across modalities; 3) A unique information dynamic perception module is proposed, which employs a conditional MI projection network combined with learning distribution regularization to adaptively extract and enhance modality-specific task-relevant unique information. Experimental results demonstrate that DRLMMC outperforms existing state-of-the-art methods on multimodal benchmark datasets, exhibiting excellent generalization ability. Notably, it achieves precise disentanglement of cross-omics features in multi-omics analysis, offering a novel methodological approach for handling complex biomedical data.

Graph clustering, which aims to divide nodes in the graph into several distinct clusters, is a fundamental yet challenging task. Benefiting from the powerful representation capability of deep learning, deep graph clustering methods have achieved great success in recent years. However, the corresponding survey paper is relatively scarce, and it is imminent to make a summary of this field. From this motivation, we conduct a comprehensive survey of deep graph clustering. Firstly, we introduce formulaic definition, evaluation, and development in this field. Secondly, the taxonomy of deep graph clustering methods is presented based on four different criteria, including graph type, network architecture, learning paradigm, and clustering method. Thirdly, we carefully analyze the existing methods via extensive experiments and summarize the challenges and opportunities from five perspectives, including graph data quality, stability, scalability, discriminative capability, and unknown cluster number. Besides, the applications of deep graph clustering methods in six domains, including computer vision, natural language processing, recommendation systems, social network analyses, bioinformatics, and medical science, are presented. Last but not least, this paper provides open resource supports, including 1) a collection (https://github.com/yueliu1999/Awesome-Deep-Graph-Clustering) of state-of-the-art deep graph clustering methods (papers, codes, and datasets) and 2) a flexible and extensible Python library (https://github.com/Marigoldwu/PyDGC) for deep graph clustering. We hope this work can serve as a quick guide and help researchers overcome challenges in this vibrant field.

Multi-view clustering aims to leverage complementary information from multiple data sources to improve clustering quality. Traditional graph-based Non-k multi-view clustering methods enable automatic cluster number determination but suffer from severe scalability issues due to their reliance on constructing large sample-level affinity graphs with quadratic complexity. To address this limitation, we propose a novel scalable Non-k Multi-View Clustering framework via Inter-Anchor Graph learning (MVC-IAG). Our method first extracts a small set of representative anchors via k-means on concatenated multi-view features, then learns a unified inter-anchor graph by integrating multi-view structural information and feature similarity priors. Our framework performs Non-k cluster discovery directly on this compact, learned inter-anchor graph, thereby enabling automatic cluster number determination, and subsequently propagates the results to all samples. Extensive experiments on multiple large scale datasets demonstrate that MVC-IAG significantly reduces computational cost while achieving competitive or superior clustering performance compared to state-of-the-art Non-k multi view clustering approaches.

Anchor graph learning has become a widely used technique for significantly reducing the computational complexity in existing multi-view clustering methods. However, most existing approaches select anchors independently for each view and then generate the consensus graph by directly fusing all anchor graphs. This process overlooks the correspondence between anchor sets across different views, i.e., the column order correspondence of the anchor graphs. To address this limitation, we propose a novel anchor-based tensor multi-rank constraint multi-view clustering method (TMC). Specifically, TMC captures the high-order structural information of the original data by constructing an anchor graph tensor and enforcing a multi-rank constraint to induce a block-diagonal structure. Additionally, to enhance anchor consistency across all view, we construct the anchor graph of each view into an anchor tensor and impose a low-rank constraint on it. In this way, the block-diagonal structure of each anchor graph maintains an approximate alignment between anchors. Furthermore, we provide theoretical proof that the generated anchor graphs inherently exhibit a block-diagonal structure. Extensive experimental results on six multi-view datasets demonstrate that TMC outperforms existing state-of-the-art methods, highlighting its effectiveness in multi-view clustering task.

Multi-View Clustering (MVC) has gained increasing attention due to its ability to effectively leverage the complementary information of multi-view data. Despite the success of existing MVC methods in many real-world applications, they often overlook the discrepancy of view-specific latent distribution and struggle to ensure the completeness of the multi-view data. To address these challenges and harness the powerful feature extraction capability of deep networks, we propose a novel Contrastive and Dual Adversarial Representation Learning method for Multi-view Clustering, termed as CDARL, to solve multi-view clustering problems with both complete and incomplete multi-view data. Specifically, CDARL employs alternating adversarial and contrastive learning to align the view-specific representations, driving them into the same semantic latent space to minimize the discrepancy in view-specific distributions. In addition, a consensus latent representation is learned by an adaptive fusion block that integrates information from multiple views. The consensus representation is further refined through adversarial learning modeling the transformation of the standard Gaussian distribution to the original data distribution. Moreover, the proposed method incorporates an imputation strategy designed to handle the incomplete multi-view data clustering task. This strategy utilizes both reconstructed samples and cross-view neighbors to impute missing views from the latent space and the original space, thereby preserving clustering information, which ensures the quality and feasibility of the imputed samples. Experimental results on six widely used datasets have verified the competitiveness of the proposed CDARL method against state-of-the-art methods in MVC problems with complete and incomplete multi-view data. Code is available athttps://github.com/xywy220/CDARL-MVC.

Spatially resolved transcriptomics (SRT) facilitates the simultaneous acquisition of gene expression profiles, spatial location, and histology images for spatial clustering analysis, providing transformative insights into cellular interactions and the underlying mechanisms of disease progression. Despite the success of existing research in spatial clustering tasks, most methods overlook the information imbalance arising among spots in intra- and inter-modal communication due to insufficient sequencing depth and modality discrepancies. To this end, we propose a novel multi-view information rebalance learning method for SRT data clustering, referred to as MIRL. Specifically, we construct hypergraphs for the gene and histological image modalities and leverage hypergraph neural networks to learn the hypergraph features, which helps mitigate the propagation of intra-modal information imbalance by capturing higher-order interactions among multiple spots, rather than relying solely on pairwise relationships in traditional feature graphs. To enhance the global coordination among spots and the interrelations between features across modalities, we perform intra-modal adaptive fusion of modality-specific hypergraph features and spatial features, followed by cross-modal integration. Furthermore, adaptive reconstruction of the cross-modal heterogeneous graph is employed to rebalance inter-modal information flow associated with pseudo-labels, ensuring more reliable information extraction by alleviating the impact of incorrect heterogeneous negative edges connections through the construction of hypergraph edges. Extensive experimental results demonstrate that the proposed MIRL achieves competitive performance in spatial domain identification compared to other state-of-the-art ones.

Imbalance in multi-domain multi-modal settings remains a significant challenge in real-world applications, such as fake news detection. Although existing methods have enhanced semantic representations and employed complex architectures to improve recognition performance, most of them focus on domain heterogeneity and modality sensitivity. However, these methods neglect the class imbalance, which severely impacts model robustness and generalization in open-world scenarios. Learning for such imbalanced multi-domain multi-modal data is crucial but rarely studied, particularly when the domain and modal of samples are imbalanced. To address these challenges, we propose a novel Imbalanced multi-domain multi-modal learning method with Expert Collaboration and Dynamic Fusion, named IECDF. Specifically, to alleviate inter-domain imbalance, we design an expert collaboration module with a domain-shared specific embedding structure and an improved gating strategy to enhance the discriminative power of the cross-domain features. Besides, a dynamic fusion mechanism based on Mamba-Former is designed to learn adaptive weights for each modality. Moreover, to tackle the intra-class imbalance problem, we adopt asymmetric re-weighted loss—DLINEX—to increase the contributions of minority class samples and learn the unbiased decision boundary. Extensive experimental results on various datasets validate the superiority of our proposed IECDF compared to state-of-the-art methods. Our code will be available at https://github.com/Yuchen-zh/IECDF .

With the rapid growth of multimodal and multi-view data, multi-view bipartite graph clustering (MVBGC) has emerged as a promising solution for large-scale tasks, which with linear complexity. However, most methods adhere to a unidirectional “encoding” design, where the bipartite graph is directly constructed from input data. Enlightened by the prevalent encoding-decoding in deep learning, this paper rethinks existing paradigms and proposes a novel “auto-encoding” MVBGC framework, named BGAE. Our model seamlessly integrates encoding, bipartite graph learning, and decoding modules within a self-supervised learning framework. The encoding module extracts a joint representation from input data, the bipartite graph learning module learns a discriminative bipartite graph in latent semantic space, and the decoding module reconstructs the input data by the structural information. Extensive experiments verify the superiority of our novel design, particularly highlighting the critical role of “decoding” learning. This work represents the first attempt to explore encoding-decoding design in MVBGC.

Anchor-based multi-view graph clustering has recently gained popularity as an effective approach for clustering data with multiple views. However, existing methods have limitations in terms of handling inconsistent information and noise across views, resulting in an unreliable consensus representation. Additionally, post-processing is needed to obtain final results after anchor graph construction, which negatively affects clustering performance. In this paper, we propose a Robust and Consistent Anchor Graph Learning method (RCAGL) for multi-view clustering to address these challenges. RCAGL constructs a consistent anchor graph that captures inter-view commonality and filters out view-specific noise by learning a consistent part and a view-specific part simultaneously. A k-connectivity constraint is imposed on the consistent anchor graph, leading to a clear graph structure and direct generation of cluster labels without additional post-processing. Experimental results on several benchmark datasets demonstrate the superiority of RCAGL in terms of clustering accuracy, scalability to large-scale data, and robustness to view-specific noise, outperforming advanced multi-view clustering methods. Our code is publicly available at https://github.com/Tracesource/RCAGL.

Benefiting from the strong reasoning capabilities, Large language models (LLMs) have demonstrated remarkable performance in recommender systems. Various efforts have been made to distill knowledge from LLMs to enhance collaborative models, employing techniques like contrastive learning for representation alignment. In this work, we prove that directly aligning the representations of LLMs and collaborative models is suboptimal for enhancing downstream recommendation tasks performance, based on the information theorem. Consequently, the challenge of effectively aligning semantic representations between collaborative models and LLMs remains unresolved. Inspired by this viewpoint, we propose a novel plug-and-play alignment framework for LLMs and collaborative models. Specifically, we first disentangle the latent representations of both LLMs and collaborative models into specific and shared components via projection layers and representation regularization. Subsequently, we perform both global and local structure alignment on the shared representations to facilitate knowledge transfer. Additionally, we theoretically prove that the specific and shared representations contain more pertinent and less irrelevant information, which can enhance the effectiveness of downstream recommendation tasks. Extensive experimental results on benchmark datasets demonstrate that our method is superior to existing state-of-the-art algorithms.

Repetitive Negative Sequential Patterns (RNSPs) can provide critical insights into the importance of sequences. However, most current RNSP mining methods require users to set an appropriate support threshold to obtain the expected number of patterns, which is a very difficult task for the users without prior experience . To address this issue, we propose a new algorithm, TK-RNSP, to mine the Top- K RNSPs with the highest support, without the need to set a support threshold. In detail, we achieve a significant breakthrough by proposing a series of definitions that enable RNSP mining to satisfy anti-monotonicity. Then, we propose a bitmap-based Depth-First Backtracking Search (DFBS) strategy to decrease the heavy computational burden by increasing the speed of support calculation. Finally, we propose the algorithm TK-RNSP in an one-stage process, which can effectively reduce the generation of unnecessary patterns and improve computational efficiency comparing to those two-stage process algorithms. To the best of our knowledge, TK-RNSP is the first algorithm to mine Top- K RNSPs. Extensive experiments on eight datasets show that TK-RNSP has better flexibility and efficiency to mine Top- K RNSPs.

Graph neural networks (GNNs) have shown remarkable success in graph-level classification tasks. However, most of the existing GNN-based studies are based on balanced datasets, while many real-world datasets exhibit long-tailed distributions. In such datasets, the tail classes receive limited attention during training, leading to prediction bias and degraded performance. To address this issue, a range of long-tailed learning strategies have been proposed, such as data re-balancing and transfer learning. However, these approaches encounter several challenges, including insufficient representation capacity for tail classes and their evaluation solely on uniform test data, limiting their capacity to handle unknown class distributions. To tackle these challenges, we introduce a novel framework, namely Knowledge-diverse Experts (KDEX) for long-tailed graph classification. Our KDEX leverages a dynamic memory module to enable the transfer of knowledge from head to tail, which improves the representation ability of the tail. To deal with unknown test distributions, KDEX introduces a knowledge-diverse expert training approach to train experts with different capacities in managing various test distributions. Moreover, we train the hierarchical router in a self-supervised manner to dynamically aggregate each knowledge-diverse expert during testing. Experimental results on multiple benchmarks reveal that our KDEX outperforms current baselines in both standard and test-agnostic long-tailed graph classification.

Inductive relation prediction aims to predict missing connections between entities unseen during training. Recent approaches adopt binary (positive or negative) training labels, which indicate whether the query relation exists between the entities, as supervision to teach models recognizing the entity-independent relation patterns in the context (enclosed subgraph or connective path). However, we argue that in this kind of method, the trained models are guided to make relation predictions by remembering whether the query relation and its contextual relational pattern co-occur more frequently in positive or negative samples. This solution could introduce two major limitations: 1) the model struggles with long-tail combinations, i.e., the combination between query relation and the relational pattern rarely occurs during training; 2) when noisy relational patterns, which fail to provide evidence for predicting the query relation, frequently occur with the query relation in positive training samples, the model will be misled into considering the noisy relational patterns as a feature supporting the existence of the query relation. To solve these problems, we propose ToC (Thinking on Context). ToC first utilizes large language models (LLMs) to incorporate a chain of thought as an additional supervisory constraint, guiding the model to make relational predictions based on logical reasoning instead of co-occurrence frequency. Additionally, ToC employs the reasoning capabilities of LLMs to construct context-level negative samples, aiding the model in identifying and disregarding noisy relational patterns. Extensive experiments show that ToC significantly outperforms state-of-the-art methods across three widely used datasets in multiple inductive settingshttps://github.com/AI-Chen/ToC_KGC.

Multi-view clustering (MVC) has demonstrated impressive performance due to its ability to capture both consistency and diversity information among views. However, most existing techniques assume that all views are available in advance, making them inadequate for stream-view data, such as in intelligent transportation systems and medical imaging analysis, where memory constraints or privacy concerns prevent storing all previous views. Although some methods attempt to address this issue by capturing consistency information, they often fail to effectively extract diversity information and cross-view relationships. We argue that these limitations are inherent to incremental multi-view clustering (IMVC), as the inability to retain all previous views inevitably leads to insufficient information utilization, thereby compromising performance. To address these challenges, we propose a novel algorithm, termed Incremental Multi-View Clustering with Cross-View Correlation and Diversity (CDIMVC). Unlike existing methods that only retain consistency information, CDIMVC also preserves diversity information and utilizes similarity matrices to capture cross-view relationships. To implement this method, we develop three key modules: the dynamic view correlation analysis module (DVCAM), the knowledge extraction module (KEM), and the knowledge transfer module (KTM). When a new data view arrives, DVCAM first assesses its importance and correlation with historical views. Subsequently, KEM computes its consistency and diversity information by comparing it to those in the knowledge base. Finally, KTM facilitates the effective transmission of past knowledge, preventing the loss of historical information. By integrating these modules, CDIMVC can effectively capture cross-view relationships and diversity information, facilitating efficient knowledge updating and maintenance. An alternating procedure is also designed to optimize the resulting optimization problem. Experimental results show that CDIMVC exceeds state-of-the-art methods, demonstrating its effectiveness in handling stream-view data.

Multi-view clustering (MVC) for remote sensing data has demonstrated significant potential in Earth observation, given its ability to aggregate multi-source information without relying on labels. Despite achieving compelling results through the combination of deep encoders and contrastive learning, existing algorithms still face two limitations: inadequate exploration of diverse spatial relationships and inability to guide the selection of sample pairs leads to blind sampling, both of which lead to suboptimal clustering performance. To tackle these challenges, we propose a sampling enhanced contrastive multi-view clustering method for remote sensing data, namely SEC-LSRM. The proposed method incorporates long- and short-range information mining to enhance clustering performance. By aggregating shortrange information extracted through autoencoders and longrange information obtained via graph autoencoders, our method improves the sampling quality of positive and negative sample pairs. To render the extracted features more compact, a multiview correlation reduction strategy is devised to filter out irrelevant information. With the extracted comprehensive features, an adaptive sampling strategy is designed to obtain high-quality positive and negative samples. Subsequently, we select positive and negative sample pairs based on these affinity matrices with idempotence and block diagonal constraints. Moreover, we integrate the optimization of these sample pairs and contrastive learning within the same framework to achieve iterative updates of both. Experiments conducted on multiple multi-view remote sensing datasets illustrate that our proposed SEC-LSRM method achieves excellent and reliable clustering performance.

Bipartite graph clustering (BGC) has emerged as a fast-growing research in the clustering community. Despite BGC has achieved promising scalability, most variants still suffer from the following concerns: a) Susceptibility to noisy features. They construct bipartite graphs in the raw feature space, inducing poor robustness to noisy features. b) Inflexible anchor selection strategies. They usually select anchors through heuristic sampling or constrained learning methods, degrading flexibility. c) Partial structure mining. Existing methods are mainly built upon Linear Reconstruction Paradigm (LRP) from subspace clustering or Locally Linear Paradigm (LLP) from manifold learning, which partially exploit linear or locally linear structures, lacking a unified perspective to integrate global complementary structures. To this end, we propose a novel model, termedJoint Robust Embedding and Structural FusionBipartiteGraphClustering (JetBGC), which focuses on three aspects, namely robustness, flexibility, and complementarity. Concretely, we first introduce a robust embedding learning module to extract latent representation that can reduce the impact of noisy features. Then, we optimize anchors via a constraint-free strategy that can flexibly capture data distribution. Furthermore, we revisit the consistency and specificity of LRP and LLP, and design a new unified structural fusion strategy to integrate both linear and locally linear structures from a global perspective. Therefore, JetBGC unifies robust representation learning, flexible anchor optimization, and structural bipartite graph fusion in a framework. Extensive experiments on synthetic and real-world datasets validate our effectiveness against existing baselines.

In recent years, multi-view unsupervised feature selection has gained significant interest for its ability to efficiently handle multi-view datasets while offering better interpretability. However, most existing methods face the following challenges: First, the presence of noisy features in the data significantly impacts the process of learning accurate feature importance. Second, the selected features contain redundant information due to ignored redundancy between them. Third, graph structure learning is performed on all samples, resulting in large computational and space overheads, which is not conducive to expansion to large-scale data. To address these challenges, we propose a multi-view unsupervised feature selection method based on latent semantics and anchor graph learning. Specifically, this method designs a feature-weighted orthogonal regression and subspace learning framework to suppress noise interference in the consensus latent semantics discovery and anchor graph construction process, enhance the robustness of multi-view representation learning and reduce the computation of graph construction. Meanwhile, the proposed method employs explicit redundancy mitigation mechanisms that penalize discriminative weight allocation to highly correlated features. Furthermore, the proposed method unifies feature weighting, consensus latent semantics discovery, and adaptive graph learning within a multi-layer learning framework, enabling comprehensive feature importance evaluation through interactive learning between multiple layers. Finally, an efficient iterative algorithm is designed to solve the proposed model. The superiority of the proposed algorithm is demonstrated by comparing it with seven state-of-the-art algorithms on seven public multi-view datasets.

Graph-based multi-view clustering methods have demonstrated satisfying performance by effectively capturing relationships among data samples. However, most existing methods primarily emphasize direct pairwise relationships, neglecting the exploration of high-order correlations present within each view. To this end, a novel approach, called multiview clustering via high-order bipartite graph learning and tensor low-rank representation (HBGTLRR), is proposed. Specifically, we first construct high-order bipartite graphs to capture latent relationships and concatenate them into a tensor. By applying tensor nuclear norm (TNN) minimization, we obtain a low-rank representation that reduces noise and preserves high-order consistency. Subsequently, a consensus graph is constructed by adaptively fusing the high-order bipartite graphs with corresponding weights, and then a Laplacian low-rank constraint is imposed on it to effectively capture the intrinsic data structure. Finally, extensive experimental results show that HBGTLRR significantly outperforms existing methods, thereby validating the effectiveness of our proposed method.

Multi-view graph clustering (MVGC) explores pairwise correlations of entire instances and comprehensively aggregates diverse source information with optimal graph structure. One major issue of practical MVGC is the high time and space complexities prohibiting being applied on large-scale applications. As a promising solution of addressing large-scale problems, anchor-based strategy identifies small portion and key landmarks to serve as replacements for the entire dataset. Despite of its efficiency, anchors chosen across views may be semantically unaligned contrasting to naturally-aligned full sample setting, which may lead to the latter inappropriate graph fusion. Limited attention has been focused on the mentioned Multi-View Anchor-Unaligned Problem (MV-AUP) in the existing literature. In this paper, we first revisit existing multi-view anchor graph clustering frameworks and present the MV-AUP phenomenon. Then, we propose a novelMulti-viewCorrespondingAnchorGraphAlignmentFusion framework (MV-CAGAF), which elegantly solves MV-AUP with structural representation matching in multi-dimensional spaces. Further, we theoretically prove our proposed structural matching approach can be regarded as minimizing the EMD distance of the two relative anchor distributions. Based on this, we design the innovative multi-view anchor graph fusion paradigm with correspondence alignment, which inherits the linear sample complexity for scalable cross-view clustering. Our proposed MV-CAGAF achieves significant improvements with the help of the novel fusion framework on comprehensive benchmark datasets. Most importantly, the experimental results on both of the simulated and real-world datasets significantly prove the importance of cross-view alignment for large-scale multi-view clustering.

Graph anomaly detection (GAD) on attributed networks aims to capture abnormal nodes whose attributes or structures differ significantly from most nodes. The existing GAD models amplify the representation differences between normal and abnormal nodes to identify anomalies via carefully designed feature extraction modules. However, these models ignore the bottlenecks encountered by abnormal nodes in message passing. In particular, when the anomalies occurs at critical crossroads, the information of multiple nodes is compressed into a fixed-length representation, and the resulting over-squashing weakens the abnormal information. To address this, we propose an unsupervisedSTructural optimization model guided by sIMilarity reconstruction (STIM). Specifically, we define redundant edges that cause over-squashing, design the Neighbor-Structure Optimization module to filter redundant edges through the edge-dropping strategy based on critical crossroads, and optimize the graph structure to alleviate over-squashing. In addition, to alleviate the over-smoothing caused by the high inter-class node similarity of the data itself and the edge-dropping strategy, we design the Neighbor-Similarity Reconstruction module based on similarity calculation, which guides the model to expand inter-class variation. Extensive experiments on benchmark datasets show that STIM can effectively optimize message passing and improve anomaly detection performance. The source code is available athttps://github.com/Junyi-Yan/STIM.

Deep learning has been widely applied in recommender systems, which has recently achieved revolutionary progress. However, most existing learning-based methods assume that the user and item distributions remain unchanged between the training phase and the test phase. However, the distribution of user and item features can naturally shift in real-world scenarios, potentially resulting in a substantial decrease in recommendation performance. This phenomenon can be formulated as an Out-Of-Distribution (OOD) recommendation problem. To address this challenge, we propose a novelDualTest-Time-Training framework forOODRecommendation, termedDT3OR. In DT3OR, we incorporate a model adaptation mechanism during the test-time phase to carefully update the recommendation model, allowing the model to adapt specially to the shifting user and item features. To be specific, we propose a self-distillation task and a contrastive task to assist the model learning both the user’s invariant interest preferences and the variant user/item characteristics during the test-time phase, thus facilitating a smooth adaptation to the shifting features. Furthermore, we provide theoretical analysis to support the rationale behind our dual test-time training framework. To the best of our knowledge, this paper is the first work to address OOD recommendation via a test-time-training strategy. We conduct experiments on five datasets with various backbones. Comprehensive experimental results have demonstrated the effectiveness of DT3OR compared to other state-of-the-art baselines.

The ubiquity of Graph Neural Networks (GNNs) emphasizes the imperative to assess their resilience against node injection attacks, a type of evasion attacks that impact victim models by injecting nodes with fabricated attributes and structures. However, prevailing attacks face two primary limitations: (1) Sequential construction of attributes and structures results in suboptimal outcomes as structure information is overlooked during attribute construction and vice versa. (2) In black-box scenarios, where attackers lack access to victim model architecture and parameters, reliance on surrogate models degrades performance due to architectural discrepancies. To overcome these limitations, we introduce GZOO, a black-box node injection attack that leverages an adversarial graph generator, compromising both attribute and structure sub-generators. This integration crafts optimal attributes and structures by considering their mutual information, enhancing their influence when aggregating information from injected nodes. Furthermore, GZOO proposes a zeroth-order optimization algorithm leveraging prediction results from victim models to estimate gradients for updating generator parameters, eliminating the necessity to train surrogate models. Across sixteen datasets, GZOO significantly outperforms state-of-the-art attacks, achieving remarkable effectiveness and robustness. Notably, on the Cora dataset with the GCN model, GZOO achieves an impressive 95.69% success rate, surpassing the maximum 66.01% achieved by baselines.

Dynamic ensemble has significantly greater potential space to improve the classification of imbalanced data compared to static ensemble. However, dynamic ensemble schemes are far less successful than static ensemble methods in the imbalanced learning field. Through an in-depth analysis on the behavior characteristics of dynamic ensemble, we find that there are some important problems that need to be addressed to release the full potential of dynamic ensemble, including but not limited to, correcting the component classifiers’ bias towards the majority classes, increasing the proportions of the positive classifiers (i.e., the component classifiers making correct prediction) for difficult samples, and providing the accurate competence estimations on the hard-to-classify samples w.r.t the classifier pool. Inspired by these, we propose a Dynamic Ensemble Framework for imbalanced data classification (imDEF). imDEF first uses the data generation method OREM$\mathrm{_{G}}$to generate multiple artificial synthetic datasets, which have diverse class distributions by rebalancing the original imbalanced data. Based on each of such synthetic datasets, imDEF then utilizes a Classification Error-aware Self-Paced Sampling Ensemble (SPSE$\mathrm{_{CE}}$) method to gradually focus more on difficult samples, to create a low-biased classifier pool and increase the proportions of the positive classifiers for the difficult samples. Finally, imDEF constructs a referee system to achieve the competence estimations by leveraging an Ensemble Margin-aware Self-Paced Sampling Ensemble (SPSE$\mathrm{_{EM}}$) method. SPSE$\mathrm{_{EM}}$incrementally strengthens the learning of the hard-to-classify samples, so that the competent levels of component classifiers could be estimated accurately. Extensive experiments demonstrate the effectiveness of imDEF. The source codes have been made publicly available on GitHub.

Graph Neural Networks (GNNs) have achieved promising performance in semi-supervised node classification in recent years. However, the problem of insufficient supervision, together with representation collapse, largely limits the performance of the GNNs in this field. To alleviate the collapse of node representations in semi-supervised scenario, we propose a novel graph contrastive learning method, termed M ixed G raph C ontrastive N etwork (MGCN). In our method, we improve the discriminative capability of the latent embeddings by an interpolation-based augmentation strategy and a correlation reduction mechanism. Specifically, we first conduct the interpolation-based augmentation in the latent space and then force the prediction model to change linearly between samples. Second, we enable the learned network to tell apart samples across two interpolation-perturbed views through forcing the correlation matrix across views to approximate an identity matrix. By combining the two settings, we extract rich supervision information from both the abundant unlabeled nodes and the rare yet valuable labeled nodes for discriminative representation learning. Extensive experimental results on six datasets demonstrate the effectiveness and the generality of MGCN compared to the existing state-of-the-art methods. The code of MGCN is available at https://github.com/xihongyang1999/MGCN on Github.

Unsupervised multi-view bipartite graph clustering (MVBGC) is a fast-growing research, due to promising scalability in large-scale tasks. Although many variants are proposed by various strategies, a common design is to construct the bipartite graph directly from the input data, i.e. only consider the unidirectional “encoding” process. However, “encoding-decoding” mechanism is a popular design for deep learning, the most representative one is auto-encoder (AE). Enlightened by this, this paper rethinks existing MVBGC paradigms and transfers the “encoding-decoding” design into graph machine learning, and proposes a novel framework termed auto-encoding multi-view bipartite graph clustering (BGAE), which integrates encoding, bipartite graph construction, and decoding modules in a self-supervised learning manner. The encoding module extracts a latent joint representation from the input data, the bipartite graph construction module learns a bipartite graph with connectivity constraint in latent semantic space, and the decoding module recreates the input data via the bipartite graph. Therefore, our novel BGAE combines representation learning, bipartite graph learning, reconstruction learning, and label inference into a unified framework. All the modules are seamlessly integrated and mutually reinforcing for clustering-friendly purposes. Extensive experiments verify the superiority of our novel design and the significance of “decoding” process. To the best of our knowledge, this is the first attempt to explore “encoding-decoding” design in traditional MVBGC. The code is provided athttps://github.com/liliangnudt/BGAE.

Knowledge graph embedding (KGE) aims at learning powerful representations to benefit various artificial intelligence applications. Meanwhile, contrastive learning has been widely leveraged in graph learning as an effective mechanism to enhance the discriminative capacity of the learned representations. However, the complex structures of KG make it hard to construct appropriate contrastive pairs. Only a few attempts have integrated contrastive learning strategies with KGE. But, most of them rely on language models (e.g.,Bert) for contrastive pair construction instead of fully mining information underlying the graph structure, hindering expressive ability. Surprisingly, we find that the entities within a relational symmetrical structure are usually similar and correlated. To this end, we propose a knowledge graph contrastive learning framework based on relation-symmetrical structure, KGE-SymCL, which mines symmetrical structure information in KGs to enhance the discriminative ability of KGE models. Concretely, a plug-and-play approach is proposed by taking entities in the relation-symmetrical positions as positive pairs. Besides, a self-supervised alignment loss is designed to pull together positive pairs. Experimental results on link prediction and entity classification datasets demonstrate that our KGE-SymCL can be easily adopted to various KGE models for performance improvements. Moreover, extensive experiments show that our model could outperform other state-of-the-art baselines.

Anchor-based multi-view graph clustering has recently gained popularity as an effective approach for clustering data with multiple views. However, existing methods have limitations in terms of handling inconsistent information and noise across views, resulting in an unreliable consensus representation. Additionally, post-processing is needed to obtain final results after anchor graph construction, which negatively affects clustering performance. In this paper, we propose a Robust and Consistent Anchor Graph Learning method (RCAGL) for multi-view clustering to address these challenges. RCAGL constructs a consistent anchor graph that captures inter-view commonality and filters out view-specific noise by learning a consistent part and a view-specific part simultaneously. A$k$-connectivity constraint is imposed on the consistent anchor graph, leading to a clear graph structure and direct generation of cluster labels without additional post-processing. Experimental results on several benchmark datasets demonstrate the superiority of RCAGL in terms of clustering accuracy, scalability to large-scale data, and robustness to view-specific noise, outperforming advanced multi-view clustering methods. Our code is publicly available athttps://github.com/Tracesource/RCAGL.

Federated knowledge graph reasoning (FedKGR) aims to perform reasoning over different clients while protecting data privacy, drawing increasing attention to its high practical value. Previous works primarily focus on data heterogeneity, ignoring challenges from limited data scale and primitive negative sample strategies, i.e., random entity replacement, which yield low-quality negatives and zero loss issues. Meanwhile, generative adversarial networks (GANs) are widely used in different fields to generate high-quality negative samples, but no work has been developed for FedKGR. To this end, we propose a plug-and-playEntity-awareAdversarialNegative sampling strategy for FedKGR, termed FedEAN. Specifically, we are the first to adopt GANs to generate high-quality negative samples in different clients. It takes the target triplet in each batch as input and outputs high-quality negative samples, which guaranteed by the joint training of the generator and discriminator. Moreover, we design an entity-aware adaptive negative sampling mechanism based on the similarity of entity representations before and after server aggregation, which can persevere the entity global consistency across clients during training. Extensive experiments demonstrate that FedEAN excels with various FedKGR backbones, demonstrating its ability to construct high-quality negative samples and address the zero-loss issue.

Masked graph autoencoder (MGAE) has emerged as a promising self-supervised graph pre-training (SGP) paradigm due to its simplicity and effectiveness. However, existing efforts perform the mask-then-reconstruct operation in the raw data space as is done in computer vision (CV) and natural language processing (NLP) areas, while neglecting the important non-Euclidean property of graph data. As a result, the highly unstable local structures largely increase the uncertainty in inferring masked data and decrease the reliability of the exploited self-supervision signals, leading to inferior representations for downstream evaluations. To address this issue, we propose a novel SGP method termed Robust mAsked gRaph autoEncoder (RARE) to improve the certainty in inferring masked data and the reliability of the self-supervision mechanism by further masking and reconstructing node samples in the high-order latent feature space. Through both theoretical and empirical analyses, we have discovered that performing a joint mask-then-reconstruct strategy in both latent feature and raw data spaces could yield improved stability and performance. To this end, we elaborately design a masked latent feature completion scheme, which predicts latent features of masked nodes under the guidance of high-order sample correlations that are hard to be observed from the raw data perspective. Specifically, we first adopt a latent feature predictor to predict the masked latent features from the visible ones. Next, we encode the raw data of masked samples with a momentum graph encoder and subsequently employ the resulting representations to improve the predicted results through latent feature matching. Extensive experiments on seventeen datasets have demonstrated the effectiveness and robustness of RARE against state-of-the-art (SOTA) competitors across three downstream tasks. Our source code is available athttps://github.com/WxTu/RARE.

Multiple kernel clustering (MKC) enhances clustering performance by deriving a consensus partition or graph from a predefined set of kernels. Despite many advanced MKC methods proposed in recent years, the prevalent approaches involve incorporating all kernels by default to capture diverse information within the data. However, learning from all kernels may not be better than one of a few kernels, particularly since some kernels exhibit a higher proportion of noise than semantic content. Additionally, existing MKC methods, whether based on early-fusion or late-fusion approaches, predominantly rely on pairwise relationships among samples or cluster structures, neglecting potential correlations between these two aspects. To this end, we propose a multiple kernel clustering with an adaptive multi-scale partition selection method (MPS), which exploits multiple-dimensional representations and the pairwise cluster structure for clustering. By the proposed kernel selection framework, potentially harmful kernels are dynamically excluded during the kernel fusion process, and then the multi-scale partitions and similarity graphs derived from the retained kernels are utilized to facilitate the improved consensus partition generation. Finally, extensive experiments are conducted to demonstrate the effectiveness of MPS on eight benchmark datasets.

Multiple Kernel Clustering (MKC) has emerged as a prominent research domain in recent decades due to its capacity to exploit diverse information from multiple views by learning an optimal kernel. Despite the successes achieved by various MKC methods, a significant challenge lies in the computational complexity associated with generating a consensus partition from the optimal kernel matrix, typically of size$n \times n$, where$n$represents the number of samples. This computational bottleneck restricts the practical applicability of these methods when confronted with large-scale datasets. Furthermore, certain existing MKC algorithms derive the consensus partition matrix by fusing all base partitions. However, this fusion process may inadvertently overlook critical information embedded in individual base kernels, potentially leading to inferior clustering performance. In light of these challenges, we introduce an innovative and efficient multiple kernel$k$-means approach, denoted as FAMKKM. Notably, FAMKKM incorporates two approximated partition matrices instead of the original individual partition matric for each base kernel. This strategic substitution significantly reduces computational complexity. Additionally, FAMKKM leverages the original kernel information to guide the fusion of all base partitions, thereby enhancing the quality of the resulting consensus partition matrix. Finally, we substantiate the efficacy and efficiency of the proposed FAMKKM through extensive experiments conducted on six benchmark datasets. Our results demonstrate its superiority over state-of-the-art methods. The demo code of this work is publicly available athttps://github.com/WangJun2023/FAMKKM

Multiple kernel clustering (MKC) aims to extract and integrate the clustering information from a set of pre-defined kernels for handling data which cannot be linearly separated well. More precisely, existing MKC methods generally devote to learn the complementary information from a set of kernel partitions, whose feature dimensions are commonly fixed as the upper bound$n$or lower bound$c$, where$n$and$c$represents the number of samples and clusters, respectively. However, the adopting of the lower bound or upper bound generally leads to poor clustering performance caused by the lack or redundancy of clustering information carried by kernel partitions. To tackle this issue, we propose a novel late fusion multiple kernel clustering method, termed as Eigenvalue Ratio Inspired Partition Learning and Fusion for Multiple Kernel Clustering (ERMKC), in this paper. Specifically, we propose an eigenvalue ratio based criterion to guide the kernel partition learning for each single kernel matrix, which ensures more suitable feature dimensions for the learnt kernel partitions. In addition, we also propose a novel late fusion model for fusing the learnt kernel partitions optimally. Furthermore, we conduct extensive experiments on numerous benchmark datasets to evaluate the proposed ERMKC method, whose results verify the effectiveness and advantage of the proposed method compared to the other state-of-the-art methods.

This study is devoted to the design of gradient boosted fuzzy rule-based models for regression problems. Fuzzy rule-based models are built on the basis of information granules formed in the input and output spaces whose structure involves a family of conditional ‘if-then’ statements. The architecture of fuzzy rule-based models contributes to the realization of a sound tradeoff between modeling accuracy and interpretability and computing overhead. Gradient boosting paradigm has emerged as a powerful learning method realized through sequentially fitting additive base learners to current residuals in the steepest descent way. However, surprisingly, studies on the design and analysis of gradient boosted fuzzy rule-based models are still lacking. In this study, fuzzy rule-based model is regarded as a base learner. Different loss functions and their influence on the performance of the final models are explored. We also thoroughly investigate an impact of the initial quality of the rule-based model (implied by the number of rules) on the process of gradient boosting. The performance of the proposed approach is illustrated by a series of experimental studies concerning synthetic and publicly available datasets.

Clustering ensemble often provides robust and stable results without accessing original features of data, and thus has been widely studied. The conventional clustering ensemble methods often take the full multiple base partitions as inputs and provide a consensus clustering result. However, in many real-world applications, full base partitions are hard to obtain because some data may be missing in some base partitions. To tackle this problem, in this paper, we propose a novel partial clustering ensemble method, which takes the partial multiple base partitions as inputs. In this method, we simultaneously fill the missing values in the base partitions and ensemble them by fully considering the consensus and diversity. Moreover, to address the unreliability issue in the partial data scenario, we seamlessly plug it into a self-paced learning framework. The extensive experiments on benchmark data sets demonstrate the effectiveness and efficiency of the proposed method when handling incomplete data.

Spatial transcriptomics technology fully leverages spatial location and gene expression information for spatial clustering tasks. However, existing spatial clustering methods primarily concentrate on utilizing the complementary features between spatial and gene expression information, while overlooking the discriminative features during the integration process. Consequently, the discriminative capability of node representation in the gene expression features is limited. Besides, most existing methods lack a flexible combination mechanism to adaptively integrate spatial and gene expression information. To this end, we propose an end-to-end deep learning method named MAFN for spatially resolved transcriptomics data clustering via a multi-view adaptive fusion network. Specifically, we first adaptively learn inter-view complementary features from spatial and gene expression information. To improve the discriminative capability of gene expression nodes by utilizing spatial information, we employ two GCN encoders to learn intra-view specific features and design a Cross-view Correlation Reduction (CCR) strategy to filter the irrelevant information. Moreover, considering the distinct characteristics of each view, a Cross-view Attention Module (CAM) is utilized to adaptively fuse the multi-view features. Extensive experimental results demonstrate that the proposed MAFN achieves competitive performance in spatial domain identification compared to other state-of-the-art ones.

Multi-view clustering has attracted increasing attention in data mining communities. Despite superior clustering performance, we observe that existing multi-view subspace clustering methods directly fuse multi-view information in the similarity level by merging noisy affinity matrices; and isolate the processes of affinity learning, multiple information fusion and clustering. Both factors may cause insufficient utilization of multi-view information, leading to unsatisfying clustering performance. This paper proposes a novel consensus one-step multi-view subspace clustering (COMVSC) method to address these issues. Instead of directly fusing affinity matrices, COMVSC optimally integrates discriminative partition-level information, which is helpful in eliminating noise among data. Moreover, the affinity matrices, consensus representation and final clustering labels are learned simultaneously in a unified framework. Extensive experiment results on benchmark datasets demonstrate the superiority of our method over other state-of-the-art approaches.

Reasoning on temporal knowledge graphs (TKGR), aiming to infer missing events along the timeline, has been widely studied to alleviate incompleteness issues in TKG, which is composed of a series of KG snapshots at different timestamps. Two types of information, i.e., intra-snapshot structural information and inter-snapshot temporal interactions, mainly contribute to the learned representations for reasoning in previous models. However, these models fail to leverage (1) semantic correlations between relationships for the former information and (2) the periodic temporal patterns along the timeline for the latter one. Thus, such insufficient mining manners hinder expressive ability, leading to sub-optimal performances. To address these limitations, we propose a novel reasoning model, termed RPC, which sufficiently mines the information underlying the Relational correlations and Periodic patterns via two novel Correspondence units, i.e., relational correspondence unit (RCU) and periodic correspondence unit (PCU). Concretely, relational graph convolutional network (RGCN) and RCU are used to encode the intra-snapshot graph structural information for entities and relations, respectively. Besides, the gated recurrent units (GRU) and PCU are designed for sequential and periodic inter-snapshot temporal interactions, separately. Moreover, the model-agnostic time vectors are generated by time2vector encoders to guide the time-dependent decoder for fact scoring. Extensive experiments on six benchmark datasets show that RPC outperforms the state-of-the-art TKGR models, and also demonstrate the effectiveness of two novel strategies in our model.

Consensus clustering provides an elegant framework to aggregate multiple weak clustering results to learn a consensus one that is more robust and stable than a single result. However, most of the existing methods usually use all data for consensus learning, whereas ignoring the side effects caused by some unreliable or difficult data. To address this issue, in this article, we propose a novel self-paced consensus clustering method with adaptive bipartite graph learning to gradually involve data from more reliable to less reliable ones in consensus learning. At first, we construct an initial bipartite graph from the base results, where the nodes represent the clusters and instances, and the edges indicate that an instance belongs to a cluster. Then, we adaptively learn a structured bipartite graph from this initial one by self-paced learning, i.e., we automatically determine the reliability of each edge with adaptive cluster similarity measuring and involve the edges in bipartite graph learning in order of their reliability. At last, we obtain the final consensus result from the learned structured bipartite graph. We conduct extensive experiments on both toy and benchmark datasets, and the results show the effectiveness and superiority of our method. The codes of this article are released in http://Doctor-Nobody.github.io/codes/code_SCCABG.zip.

Multi-view clustering aims at integrating information from different views to improve clustering performance. Recent methods integrate multiple view-specific partition matrices to seek a consensus one and have demonstrated promising clustering performance in various applications. However, the clustering performance of such methods heavily relies on the consensus partition matrix estimated by the arithmetic mean in euclidean space and thus is highly susceptible to noise corruption. To this end, this article proposes to learn a consensus partition matrix through the geometric mean on the manifold to achieve robust clustering. Specifically, the multiple view-specific partition matrices can be regarded as points residing in the Stiefel manifold and enable a manifold-based integration. Consequently, the view-specific partition matrices are integrated by estimating a consensus partition matrix as the center point on the Stiefel manifold. Such a partition integration boils down to the Fréchet mean problem on a manifold, which is solved by the intrinsic manifold-based optimization and proves effective in providing a more robust estimation against noise. Experimental results on seven benchmark datasets demonstrate the effectiveness and noise-robustness of our proposed method in comparison to eight competitive methods.

Multi-view learning becomes increasingly attractive and promising because multimodal or multi-view data are commonly encountered in real-world applications. In this study, we develop a novel multi-view Takagi–Sugeno–Kang (TSK) fuzzy system framework to handle classification problems for such data. We propose an anchor and graph subspace clustering strategy to discover and represent the actual latent data distribution for each view separately. In this way, the discriminate anchors (landmarks) are learned to capture the main structure of the multi-view data. This strategy also provides a computationally efficient clustering algorithm with respect to the number of instances. These resulting anchors are formed as the prototypes of information granules (IGs) for fuzzy modeling. Then we construct an information-granule-based multi-view TSK fuzzy classification model inherited from the natural interpretability of fuzzy rule-based systems. Concretely, the relationship between the multi-view input and label output spaces is depicted by IGs-oriented fuzzy rules. The experimental studies involve various commonly used benchmark datasets, which indicate that our proposed method achieves comparable or better performance compared to the state-of-the-art algorithms.

Unsupervised bipartite graph learning has been a hotpot in multi-view clustering, to tackle the restricted scalability issue of traditional full graph clustering in large-scale applications. However, the existing bipartite graph clustering paradigm pays little attention to the adverse impact of noisy features on learning process. To further facilitate this part of research, apart from simply reweighting features to depress the noisy ones, we take the first step towards analyzing the induced adverse impact via theoretical and experimental investigations. One crucial finding in this paper is that the existence of noisy features will incur “anchor shift” phenomenon, which deviates the potential representations of anchors and then degrades performance. To this end, we propose a coupled noisy feature filter mechanism with automatically finding feature importance to remedy the anchor shift issue in this paper. Apart from leveraging features, we theoretically analyze the bounds of proposed feature-adaptive bipartite graph's fuzzy membership. Specifically, distinguishing features' discrimination will increase the fuzzy membership to achieve soft partitions against the potential inaccurate absolute relationship. With the afore-mentioned merits, our proposed multi-view bipartite graph clustering with coupled noisy feature filter model (MVBGC-NFF) provides novel and interesting insights on the feature level of anchor shift. The effectiveness and efficiency of MVBGC-NFF are demonstrated on synthetic and real-world datasets with improving clustering performance, increasing fuzzy membership, and filtering noisy features. The code is available onhttps://github.com/liliangnudt/MVBGC-NFF.

Online Event-based social networks (EBSNs), such as Meetup and Whova, which provide platforms for users to publish, arrange and participate in events, have become increasingly popular. A major challenge for managing EBSNs is to generate the most satisfactory event arrangement, i.e. events are scheduled at the reasonable time to attract maximum number of participants. Existing approaches usually focus on assigning a set of events organized by the same group to time intervals, but ignore the competitive relationships among different event organizers, which will lead to event time allocations unacceptable to organizers. Thus, a more intelligent EBSNs platform that allocates social events properly in a global view (i.e. the perspective of platform) is desired. In this paper, we first formally define the problem of Platform-oriented Event Time Allocation (PETA), which contains two parts: the prediction of event feasible time period and the event time allocation. Unfortunately, we find that the PETA problem is NP-hard due to the global conflict constraints on events. Thus, we propose design a greedy algorithm and two approximation algorithms to solve the PETA problem. Finally, we conduct extensive experiments on both real and synthetic datasets to test the effectiveness and efficiency of the proposed algorithms.

Multi-view spectral clustering, which exploits the complementary information among graphs of diverse views to obtain superior clustering results, has attracted intensive attention recently. However, most existing multi-view spectral clustering methods obtain the clustering partitions in a two-step scheme, i.e., spectral embedding and subsequent$k$-means. This two-step scheme inevitably seeks sub-optimal clustering results due to the information loss during the two-steps processes. Besides, existing multi-view spectral clustering methods do not jointly utilize the information of graphs and embedding matrices, which also degrades final clustering results. To solve these issues, we propose a unified one-step multi-view spectral clustering method, which integrates the spectral embedding and$k$-means into a unified framework to obtain discrete clustering labels with a one-step strategy. Under the observation that the inner product of the embedding matrix is a low-rank approximation of the graph, we combine graphs and embedding matrices of different views to obtain a unified graph. Then, we directly capture the discrete clustering indicator matrix from the unified graph. Furthermore, we design an effective optimization algorithm to solve the resultant problem. Finally, a set of experiments on various datasets are conducted to verify the effectiveness of the proposed method. The demo code of this work is publicly available atrgb]0,0,1https://github.com/guanyuezhen/UOMvSC.

This paper proposes a simple interpolation Oversampling method with the purpose of Reliably Expanding the Minority class regions (OREM). OREM first finds the candidate minority region around each original minority sample, then exploits this region to further identify those clean subregions without distributing any majority sample. The synthetic samples are only allowed to generate in the clean subregions, so that the regions of the minority class can be broadened reliably. Given that the learning from multiclass imbalanced data is more challenging as compared to two-class scenarios, we also extend OREM to handle multiclass imbalance problems by leveraging an iteration procedure of generating synthetic samples, consequently leading to a multiclass oversampling algorithm OREM-M. The key peculiarity of OREM-M is to reduce the class overlapping not only between the synthetic minority and original samples, but also from the synthetic samples of different minority classes. In this way, OREM-M ensures that the data of each class after oversampling can be modeled well. In addition, we embed OREM into boosting framework to develop a new ensemble method OREMBoost addressing class imbalance problems. Extensive experiments demonstrate the effectiveness of the proposed OREM, OREM-M, and OREMBoost.

Online Event-based social networks (EBSNs), such as Meetup and Whova, which provide platforms for users to publish, arrange and participate in events, have become increasingly popular. A major challenge for managing EBSNs is to generate the most satisfactory event arrangement. Existing approaches usually focus on assigning a set of events organized to time intervals, but ignore the competitive relationships among different event organizers, which will lead to event time allocations unacceptable to organizers. Thus, a more intelligent EBSNs platform that allocates social events properly in a global view (i.e. the perspective of platform) is desired. In this work, we first formally define the problem of Platform-oriented Event Time Allocation (PETA), which contains two parts: the prediction of event feasible time period and the event time allocation. We propose a method to calculate event feasible time period based on event time prediction, and design a greedy algorithm and two approximation algorithms to solve the PETA problem. Extensive experiments on both real and synthetic datasets demonstrate that the proposed algorithms have high effectiveness and efficiency.

Location prediction has attracted much attention due to its important role in many location-based services, including taxi services, route navigation, traffic planning, and location-based advertisements. Traditional methods only use spatial-temporal trajectory data to predict where a user will go next. The divorce of semantic knowledge from the spatial-temporal one inhibits our better understanding of users’ activities. Inspired by the architecture of Long Short Term Memory (LSTM), we design ST-LSTM, which draws on semantic trajectories to predict future locations. Semantic data add a new dimension to our study, increasing the accuracy of prediction. Since semantic trajectories are sparser than the spatial-temporal ones, we propose a strategic filling algorithm to solve this problem. In addition, as the prediction is based on the historical trajectories of users, the cold-start problem arises. We build a new virtual social network for users to resolve the issue. Experiments on two real-world datasets show that the performance of our method is superior to those of the baselines.

Multi-view spectral clustering can effectively reveal the intrinsic cluster structure among data by performing clustering on the learned optimal embedding across views. Though demonstrating promising performance in various applications, most of existing methods usually linearly combine a group of pre-specified first-order Laplacian matrices to construct the optimal Laplacian matrix, which may result in limited representation capability and insufficient information exploitation. Also, storing and implementing complex operations on the{$n\times n}$Laplacian matrices incurs intensive storage and computation complexity. To address these issues, this paper first proposes a multi-view spectral clustering algorithm that learns a high-order optimal neighborhood Laplacian matrix, and then extends it to the late fusion version for accurate and efficient multi-view clustering. Specifically, our proposed algorithm generates the optimal Laplacian matrix by searching the neighborhood of the linear combination of both the first-order and high-order base Laplacian matrices simultaneously. By this way, the representative capacity of the learned optimal Laplacian matrix is enhanced, which is helpful to better utilize the hidden high-order connection information among data, leading to improved clustering performance. We design an efficient algorithm with proved convergence to solve the resultant optimization problem. Extensive experimental results on nine datasets demonstrate the superiority of the proposed algorithm

Multiple kernel clustering (MKC) algorithm aims to group data into different categories by optimally integrating information from a group of pre-specified kernels. Though demonstrating superiorities in various applications, we observe that existing MKC algorithms usuallydo not sufficiently consider the local density around individual data samplesandexcessively limit the representation capacity of the learned optimal kernel, leading to unsatisfying performance. In this paper, we propose an algorithm, called optimal neighborhood MKC with adaptive local kernels (ON-ALK), to address the two issues. In specific, we construct adaptive local kernels to sufficiently consider the local density around individual data samples, where different numbers of neighbors are discriminatingly selected on each sample. Further, the proposed ON-ALK algorithm boosts the representation of the learned optimal kernel via relaxing it into the neighborhood area of weighted combination of the pre-specified kernels. To solve the resultant optimization problem, a three-step iterative algorithm is designed and theoretically proven to be convergent. After that, we also study the generalization bound of the proposed algorithm. Extensive experiments have been conducted to evaluate the clustering performance. As indicated, the algorithm significantly outperforms state-of-the-art methods in recent literatures on six challenging benchmark datasets, verifying its advantages and effectiveness.

Although demonstrating great success, previous multi-view unsupervised feature selection (MV-UFS) methods often construct a view-specific similarity graph and characterize the local structure of data within each single view. In such a way, the cross-view information could be ignored. In addition, they usually assume that different feature views are projected from a latent feature space while the diversity of different views cannot be fully captured. In this work, we resent a MV-UFS model via cross-view local structure preserved diversity and consensus learning, referred to as CvLP-DCL briefly. In order to exploit both the shared and distinguishing information across different views, we project each view into a label space, which consists of a consensus part and a view-specific part. Therefore, we regularize the fact that different views represent same samples. Meanwhile, a cross-view similarity graph learning term with matrix-induced regularization is embedded to preserve the local structure of data in the label space. By imposing the$l_{2,1}$-norm on the feature projection matrices for constraining row sparsity, discriminative features can be selected from different views. An efficient algorithm is designed to solve the resultant optimization problem and extensive experiments on six publicly datasets are conducted to validate the effectiveness of the proposed CvLP-DCL.

Multi-view clustering has attracted increasing attention in multimedia, machine learning and data mining communities. As one kind of the essential multi-view clustering algorithm, multi-view subspace clustering (MVSC) becomes more and more popular due to its strong ability to reveal the intrinsic low dimensional clustering structure hidden across views. Despite superior clustering performance in various applications, we observe that existing MVSC methodsdirectly fuse multi-view information in the similarity level by merging noisy affinity matrices; andisolate the processes of affinity learning, multi-view information fusion and clustering. Both factors may cause insufficient utilization of multi-view information, leading to unsatisfying clustering performance. This paper proposes a novel consensus one-step multi-view subspace clustering (COMVSC) method to address these issues. Instead of directly fusing multiple affinity matrices, COMVSC optimally integrates discriminative partition-level information, which is helpful to eliminate noise among data. Moreover, the affinity matrices, consensus representation and final clustering labels matrix are learned simultaneously in a unified framework. By doing so, the three steps can negotiate with each other to best serve the clustering task, leading to improved performance. Accordingly, we propose an iterative algorithm to solve the resulting optimization problem. Extensive experiment results on benchmark datasets demonstrate the superiority of our method against other state-of-the-art approaches.

Deep autoencoder-based methods are the majority of deep anomaly detection. An autoencoder learning on training data is assumed to produce higher reconstruction error for the anomalous samples than the normal samples and thus can distinguish anomalies from normal data. However, this assumption does not always hold in practice, especially in unsupervised anomaly detection, where the training data is anomaly contaminated. We observe that the autoencoder generalizes so well on the training data that it can reconstruct both the normal data and the anomalous data well, leading to poor anomaly detection performance. Besides, we find that anomaly detection performance is not stable when using reconstruction error as anomaly score, which is unacceptable in the unsupervised scenario. Because there are no labels to guide on selecting a proper model. To mitigate these drawbacks for autoencoder-based anomaly detection methods, we propose an Improved AutoEncoder for unsupervised Anomaly Detection (IAEAD). Specifically, we manipulate feature space to make normal data points closer using anomaly detection-based loss as guidance. Different from previous methods, by integrating the anomaly detection-based loss and autoencoder's reconstruction loss, IAEAD can jointly optimize for anomaly detection tasks and learn representations that preserve the local data structure to avoid feature distortion. Experiments on five image data sets empirically validate the effectiveness and stability of our method.

Multiview clustering (MVC) collects complementary and abundant information, which draws much attention in machine learning and data mining community. Existing MVC methods usually hold the assumption that all the views are complete. However, multiple source data are often incomplete in real-world applications, and so on sensor failure or unfinished collection process, which gives rise to incomplete multiview clustering (IMVC). Although enormous efforts have been devoted in IMVC, there still are some urgent issues that need to be solved: (i) The locality among multiple views has not been utilized in the existing mechanism; (ii) Existing methods inappropriately force all the views to share consensus representation while ignoring specific structures. In this paper, we propose a novel method termed partial MVC with locality graph regularization to address these issues. First, followed the traditional IMVC approaches, we construct weighted semi-nonnegative matrix factorization models to handle incomplete multiview data. Then, upon the consensus representation matrix, the locality graph is constructed for regularizing the shared feature matrix. Moreover, we add the coefficient regression term to constraint the various base matrices among views. We incorporate the three aforementioned processes into a unified framework, whereas they can negotiate with each other serving for learning tasks. An effective iterative algorithm is proposed to solve the resultant optimization problem with theoretically guaranteed convergence. The comprehensive experiment results on several benchmarks demonstrate the effectiveness of the proposed method.

Multiple kernel clustering (MKC) methods aim at integrating an optimal partition from a set of precalculated kernel matrices. Though achieving success in various applications, we observe that existing MKC methods: (i) lack of representation flexibility; and (ii) do not considerably preserve the locality structure in partition space. These issues may adversely affect the learning procedure of MKC, leading to unsatisfying clustering performance. In this paper, we propose a late fusion MKC method with local graph refinement to address the aforementioned issues. Different from existing MKC mechanisms, our method unifies the traditional weighted multiple kernel k-means, kernel partition, and graph construction into a single optimization procedure. The local graph is utilized to preserve the locality information in partition space and therefore all of the counterparts can be boosted for mutual clustering improvements. By this way, our approach enhances the local graph structure in partition space and enjoys more flexible kernel representations, leading to significant clustering improvements. Moreover, a three-step alternate algorithm is developed to solve the resultant optimization problem with proved convergence. Extensive experiments are conducted on several multiple kernel benchmark datasets to compare the proposed algorithm with the state-of-the-art ones, and the results well demonstrate its effectiveness and superiority.

Recently, convex-concave bilinear Saddle Point Problems (SPP) is widely used in lasso problems, Support Vector Machines, game theory, and so on. Previous researches have proposed many methods to solve SPP, and present their convergence rate theoretically. To achieve linear convergence, analysis in those previouse studies requires strong convexity of φ( z ). But, we find the linear convergence can also be achieved even for a general convex but not strongly convex φ( z ). In the article, by exploiting the strong duality of SPP, we propose a new method to solve SPP, and achieve the linear convergence. We present a new general sufficient condition to achieve linear convergence, but do not require the strong convexity of φ( z ). Furthermore, a more efficient method is also proposed, and its convergence rate is analyzed in theoretical. Our analysis shows that the well conditioned φ( z ) is necessary to improve the efficiency of our method. Finally, we conduct extensive empirical studies to evaluate the convergence performance of our methods.

Similarity representation plays a central role in increasingly popular anomaly detection techniques, which have been successfully applied in various realistic scenes. Until now, many low-rank representation techniques have been introduced to measure the similarity relations of data; yet, they only concern to minimize reconstruction errors, without involving the structural information of data. Besides, the traditional low-rank representation methods often take nuclear norm as their low-rank constraints, easily yielding a suboptimal solution. To address the problems above, in this article, we propose a novel anomaly detection method, which exploits kernel preserving embedding, as well as the double nuclear norm, to explore the similarity relations of data. Based on the similarity relations, a kind of probability transition matrix is derived, and a tailored random walk is further adopted to reveal anomalies. The proposed method can not only preserve the manifold structural properties of the data, but also alleviate the suboptimal problem. To validate the superiority of our method, extensive experiments with eight popular anomaly detection algorithms were conducted on 12 widely used datasets. The experimental results show that our detection method outperformed the state-of-the-art anomaly detection algorithms in most cases.

Simultaneous clustering and optimization (SCO) has recently drawn much attention due to its wide range of practical applications. Many methods have been previously proposed to solve this problem and obtain the optimal model. However, when a dataset evolves over time, those existing methods have to update the model frequently to guarantee accuracy; such updating is computationally infeasible. In this paper, we propose a new formulation of SCO to handle evolving datasets. Specifically, we propose a new variant of the alternating direction method of multipliers (ADMM) to solve this problem efficiently. The guarantee of model accuracy is analyzed theoretically for two specific tasks: ridge regression and convex clustering. Extensive empirical studies confirm the effectiveness of our method.

As a metric to measure the performance of an online method, dynamic regret with switching cost has drawn much attention for online decision making problems. Although the sublinear regret has been provided in much previous research, we still have little knowledge about the relation between the dynamic regret and the switching cost . In the article, we investigate the relation for two classic online settings: Online Algorithms (OA) and Online Convex Optimization (OCO). We provide a new theoretical analysis framework that shows an interesting observation; that is, the relation between the switching cost and the dynamic regret is different for settings of OA and OCO. Specifically, the switching cost has significant impact on the dynamic regret in the setting of OA. But it does not have an impact on the dynamic regret in the setting of OCO. Furthermore, we provide a lower bound of regret for the setting of OCO, which is same with the lower bound in the case of no switching cost. It shows that the switching cost does not change the difficulty of online decision making problems in the setting of OCO.

Deep clustering gains superior performance than conventional clustering by jointly performing feature learning and cluster assignment. Although numerous deep clustering algorithms have emerged in various applications, most of them fail to learn robust cluster-oriented features which in turn hurts the final clustering performance. To solve this problem, we propose a two-stage deep clustering algorithm by incorporating data augmentation and self-paced learning. Specifically, in the first stage, we learn robust features by training an autoencoder with examples that are augmented by random shifting and rotating the given clean examples. Then, in the second stage, we encourage the learned features to be cluster-oriented by alternatively finetuning the encoder with the augmented examples and updating the cluster assignments of the clean examples. During finetuning the encoder, the target of each augmented example in the loss function is the center of the cluster to which the clean example is assigned. The targets may be computed incorrectly, and the examples with incorrect targets could mislead the encoder network. To stabilize the network training, we select most confident examples in each iteration by utilizing the adaptive self-paced learning. Extensive experiments validate that our algorithm outperforms the state of the arts on four image datasets.

Feature extraction and feature selection have been regarded as two independent dimensionality reduction methods in most of the existing literature. In this paper, we propose to integrate both approaches into a unified framework and design an unsupervised linear feature selective projection (FSP) for feature extraction with low-rank embedding and dual Laplacian regularization, with the aim to exploit the intrinsic relationship among data and suppress the impact of noise. Specifically, a projection matrix with an l2,1-norm regularization is introduced to project original high dimensional data points into a new subspace with lower dimension, where the l2,1-norm regularization can endow the projection with good interpretability. We deploy a coefficient matrix with low rank constraint to reconstruct the data points and the l2,1-norm is imposed to regularize the data reconstruction errors in the low-dimensional subspace and make FSP robust to noise. Furthermore, a dual graph Laplacian regularization term is imposed on the low dimensional data and data reconstruction matrix for preserving the local manifold geometrical structure of data. Finally, an alternatively iterative algorithm is carefully designed for solving the proposed optimization model. Theoretical convergence and computational complexity analysis of the algorithm are also provided. Comprehensive experiments on various benchmark datasets have been carried out to evaluate the performance of the proposed FSP. As indicated, our algorithm significantly outperforms other state-of-the-art methods for feature extraction.

Recently, network lasso has dawn much attention due to its remarkable performance on simultaneous clustering and optimization. However, it usually suffers from the imperfect data (noise, missing values, etc.), and yields sub-optimal solutions. The reason is that it finds the similar instances according to their features directly, which is usually impacted by the imperfect data, and thus returns sub-optimal results. In this paper, we propose triangle lasso to avoid its disadvantage for graph datasets. In a graph dataset, each instance is represented by a vertex. If two instances have many common adjacent vertices, they tend to become similar. Although some instances are profiled by the imperfect data, it is still able to find the similar counterparts. Furthermore, we develop an efficient algorithm based on Alternating Direction Method of Multipliers (ADMM) to obtain a moderately accurate solution. In addition, we present a dual method to obtain the accurate solution with the low additional time consumption. We demonstrate through extensive numerical experiments that triangle lasso is robust to the imperfect data. It usually yields a better performance than the state-of-the-art method when performing data analysis tasks in practical scenarios.

Compressive spectral clustering (CSC) efficiently leverages graph filter and random sampling techniques to speed up clustering process. However, we find that CSC algorithm suffers from two main problems: i) The direct use of the dichotomy and eigencount techniques for estimating laplacian matrix’s k-th eigenvalue is expensive. ii) The computation of polynomial approximation repeats in each iteration for every cluster in the interpolation process, which occupies most of the computation time of CSC. To address these problems, we propose a new approach called FCSC for fast compressive spectral clustering. FCSC addresses the first problem by assuming that the eigenvalues approximately satisfy local uniform distribution, and addresses the second problem by recalculating the pairwise similarity between nodes with low-dimensional representation to reconstruct denoised laplacian matrix. The time complexity of reconstruction is linear with the number of non-zeros in laplacian matrix. As experimentally demonstrated on artificial and real-world datasets, our approach significantly reduces the computation time while preserving high clustering accuracy comparable to previous designs, verifying the effectiveness of FCSC.