Multiplex heterogeneous networks are common in real-world scenarios, where entities interact through diverse types of relations across multiple semantic layers. Recent advances in multiplex heterogeneous graph neural networks have achieved remarkable results by incorporating node and relation types into message passing and designing relation-aware architectures. However, most existing methods either decouple relations and risk losing complex semantics or require handcrafted relation patterns, which limit scalability. Moreover, prevailing models are typically restricted to Euclidean space, making it difficult to capture non-Euclidean topologies and to distinguish complex interactions among heterogeneous nodes and relations. Standard GNN message passing, grounded in the homophily assumption, also proves inadequate for the intricate, coupled structures in multiplex heterogeneous graphs. To address these challenges, we propose MRiemGNN, a novel multiplex heterogeneous graph neural network that synergizes Euclidean and Riemannian spaces through a geometry-aware, relation-specific message passing scheme and cross-space mutual learning. Experiments on multiple real-world datasets show that MRiemGNN achieves superior performance, efficiency, and scalability on both node classification and link prediction tasks.

Sequential recommendation models analyze user historical behavior sequences to capture temporal dependencies and the dynamic evolution of interests, enabling accurate predictions of future behaviors. However, there are still two critical challenges that remain unsolved: i) Inadequate temporal modeling of user intent, which fails to distinguish between global intent tendency and temporal contextual intent. ii) Noise in sequential interaction data may introduce bias into the model. To address these issues, we propose a Self-Supervised Hypergraph Sequential Recommendation Framework (S2HyRec). This framework features the Global Intent Tendency module for capturing long-term preferences, the Temporal Contextual Intent module for modeling dynamic time-sensitive interests. Additionally, we develop the Sequence Dependency-Aware module that analyzes the chronological flow of interactions to uncover inherent behavioral dynamics, further enriching the comprehensive user intent representation. To mitigate noisy interactions, we employ a Cross-View Self-Supervised Learning module that enhances the model's ability to distinguish genuine preferences from noise. Extensive experiments on four benchmark datasets demonstrate the superiority of S2HyRec over various state-of-the-art recommendation methods, especially achieving average improvements of 15.13% and 14.03% in NDCG@10 and NDCG@20, respectively, across the four datasets.

The prevalence of real-world multi-view data makes incomplete multi-view clustering (IMVC) a crucial research. The rapid development of Graph Neural Networks (GNNs) has established them as one of the mainstream approaches for multi-view clustering. Despite significant progress in GNNs-based IMVC, some challenges remain: (1) Most methods rely on the K-Nearest Neighbors (KNN) algorithm to construct static graphs from raw data, which introduces noise and diminishes the robustness of the graph topology. (2) Existing methods typically utilize the Mean Squared Error (MSE) loss between the reconstructed graph and the sparse adjacency graph directly as the graph reconstruction loss, leading to substantial gradient noise during optimization. To address these issues, we propose a novel Dynamic Deep Graph Learning for Incomplete Multi-View Clustering with Masked Graph Reconstruction Loss (DGIMVCM). Firstly, we construct a missing-robust global graph from the raw data. A graph convolutional embedding layer is then designed to extract primary features and refined dynamic view-specific graph structures, leveraging the global graph for imputation of missing views. This process is complemented by graph structure contrastive learning, which identifies consistency among view-specific graph structures. Secondly, a graph self-attention encoder is introduced to extract high-level representations based on the imputed primary features and view-specific graphs, and is optimized with a masked graph reconstruction loss to mitigate gradient noise during optimization. Finally, a clustering module is constructed and optimized through a pseudo-label self-supervised training mechanism. Extensive experiments on multiple datasets validate the effectiveness and superiority of DGIMVCM.

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.

As a basic machine learning task, Multi-View Classification (MVC) has garnered considerable attention and achieved great success. However, the existing MVC methods, especially late fusion style ones still suffer from some problems: 1) hidden valuable information is not well exploited; 2) a lack of interaction before decision making. To address these problems, we propose a novel framework named ”TrashtoTreasure” that leverages mutual information to effectively exploit hidden valuable information. Specifically, the framework explicitly disentangles multi-view information into ”useful” components and ”trash” (noisy) components, and further extracts potentially valuable ”treasure” information from the ”trash”components of all views. Additionally, we design a tailored objective function that facilitates the effective separation of ”useful” and ”trash” components, as well as the synergistic extraction of ”treasure” information. This function guides model optimization through triple mutual information constraints. Experimental results on synthetic data and several real-world data sets verified the effectiveness and superiority of the proposed method. The fresh perspective offered by this article may inspire more interesting exploration in this direction. The codes are available athttps://github.com/jiezhang054/TrashToTreasure.

Time series anomaly detection is crucial in fields such as industrial monitoring, financial risk management, and network security. Graph Neural Networks (GNNs) have demon strated strong capabilities in capturing multivariate dependencies. However, existing methods often fail to adequately account for the temporal proximity between adjacent time points and are susceptible to the influence of weak or noisy connections during graph-based representation learning. To address these challenges, we propose LaGraph, a novel framework that integrates GNNs with a mask-optimized attention mechanism. Specifically, LaGraph decomposes input sequences into stable and trend components using an Expert Decomposition Block. The trend component is processed via a Multi-layer Convolution Block, while the stable component is modeled with a Proximity enhanced Graph Convolutional Network that incorporates a Laplacian kernel to capture local temporal dependencies. Additionally, a Mask-optimized Multi-head Attention Block, based on the Straight-Through Estimator (STE), mitigates the negative effects of less informative edges, enhancing both representation quality and reconstruction performance. Extensive experiments on five real-world benchmark datasets demonstrate that La Graph consistently outperforms state-of-the-art methods, veri fying its effectiveness and superiority for time series anomaly detection. To promote reproducibility and support future research, we have publicly released the full implementation at https://github.com/hit-zsc/LaGraph.

Incomplete multi-view clustering has become one of the important research problems due to the extensive missing multi-view data in the real world. Although the existing methods have made great progress, there are still some problems: 1) most methods cannot effectively mine the information hidden in the missing data; 2) most methods typically divide representation learning and clustering into two separate stages, but this may affect the clustering performance as the clustering results directly depend on the learned representation. To address these problems, we propose a novel incomplete multi-view clustering method with hierarchical information transfer. Firstly, we design the view-specific Graph Convolutional Networks (GCN) to obtain the representation encoding the graph structure, which is then fused into the consensus representation. Secondly, considering that one layer of GCN transfers one-order neighbor node information, the global graph propagation with the consensus representation is proposed to handle the missing data and learn deep representation. Finally, we design a weight-sharing pseudo-classifier with contrastive learning to obtain an end-to-end framework that combines view-specific representation learning, global graph propagation with hierarchical information transfer, and contrastive clustering for joint optimization. Extensive experiments conducted on several commonly-used datasets demonstrate the effectiveness and superiority of our method in comparison with other state-of-the-art approaches.

Large Language Models (LLMs) have recently achieved impressive results in complex reasoning tasks through Chain of Thought (CoT) prompting. However, most existing CoT methods rely on using the same prompts, whether manually designed or automatically generated, to handle the entire dataset. This one-size-fits-all approach may fail to meet the specific needs arising from the diversities within a single dataset. To solve this problem, we propose the Clustered Distance-Weighted Chain of Thought (CDW-CoT) method, which dynamically constructs prompts tailored to the characteristics of each data instance by integrating clustering and prompt optimization techniques. Our method employs clustering algorithms to categorize the dataset into distinct groups, from which a candidate pool of prompts is selected to reflect the inherent diversity within the dataset. For each cluster, CDW-CoT trains the optimal prompt probability distribution tailored to their specific characteristics. Finally, it dynamically constructs a unique prompt probability distribution for each test instance, based on its proximity to cluster centers, from which prompts are selected for reasoning. CDW-CoT consistently outperforms traditional CoT methods across six datasets, including commonsense, symbolic, and mathematical reasoning tasks. Specifically, when compared to manual CoT, CDW-CoT achieves an average accuracy improvement of 25.34% on LLaMA2 (13B) and 15.72% on LLaMA3 (8B).

In real-world scenarios, complex data such as multispectral images and multi-frame videos inherently exhibit robust low-rank property. This property is vital for multi-dimensional inverse problems, such as tensor completion, spectral imaging reconstruction, and multispectral image denoising. Existing tensor singular value decomposition (t-SVD) definitions rely on hand-designed or pre-given transforms, which lack flexibility for defining tensor nuclear norm (TNN). The TNN-regularized optimization problem is solved by the singular value thresholding (SVT) operator, which leverages the t-SVD framework to obtain the low-rank tensor. However, it's quite complicated to introduce SVT into deep neural network due to the numerical instability problem in solving the derivatives of the eigenvectors. In this paper, we introduce a novel data-driven generative low-rank t-SVD model based on the learnable orthogonal transform, which can be naturally solved under its representation. Prompted by the linear algebra theorem of the Householder transformation, our learnable orthogonal transform is achieved by constructing an endogenously orthogonal matrix adaptable to neural networks, optimizing it as arbitrary orthogonal matrices. Additionally, we propose a low-rank solver as a generalization of SVT, which utilizes an efficient representation of generative networks to obtain low-rank structures. Extensive experiments highlight its significant restoration enhancements.

Previous successful approaches to missing modality completion rely on carefully designed fusion techniques and extensive pre-training on complete data, which can limit their generalizability in out-of-domain (OOD) scenarios. In this study, we pose a new challenge: can we develop a missing modality completion model that is both resource-efficient and robust to OOD generalization? To address this, we present a training-free framework for missing modality completion that leverages large multimodal model (LMM). Our approach, termed the "Knowledge Bridger", is modality-agnostic and integrates generation and ranking of missing modalities. By defining domain-specific priors, our method automatically extracts structured information from available modalities to construct knowledge graphs. These extracted graphs connect the missing modality generation and ranking modules through the LMM, resulting in high-quality imputations of missing modalities. Experimental results across both general and medical domains show that our approach consistently outperforms competing methods, including in OOD generalization. Additionally, our knowledge-driven generation and ranking techniques demonstrate superiority over variants that directly employ LMMs for generation and ranking, offering insights that may be valuable for applications in other domains.

Large Language Models (LLMs) require robust evaluation.However, existing frameworks often rely on curated datasets that, once public, may be accessed by newer LLMs.This creates a risk of data leakage, where test sets inadvertently become part of training data, compromising evaluation fairness and integrity.To mitigate this issue, we propose Behave as Claimed (BaC), a novel evaluation framework inspired by counterfactual reasoning.BaC constructs a "what-if" scenario where LLMs respond to counterfactual questions about how they would behave if the input were manipulated.We refer to these responses as claims, which are verifiable by observing the LLMs' actual behavior when given the manipulated input.BaC dynamically generates and verifies counterfactual questions using various few-shot in-context learning evaluation datasets, reducing their susceptibility to data leakage.Moreover, BaC provides a more challenging evaluation paradigm for LLMs.LLMs must thoroughly understand the prompt, the task, and the consequences of their responses to achieve better performance.We evaluate several LLMs and find that, while most perform well on the original datasets, they struggle with BaC.This suggests that LLMs usually fail to align their claims with their actual behavior and that high performance on standard datasets may be less stable than previously assumed.

Federated multi-view clustering has been proposed to mine the valuable information within multi-view data distributed across different devices and has achieved impressive results while preserving the privacy. Despite great progress, most federated multi-view clustering methods only used global pseudo-labels to guide the downstream clustering process and failed to exploit the global information when extracting features. In addition, missing data problem in federated multi-view clustering task is less explored. To address these problems, we propose a novel Federated Incomplete Multi-view Clustering method with globally Fused Graph guidance (FIMCFG). Specifically, we designed a dual-head graph convolutional encoder at each client to extract two kinds of underlying features containing global and view-specific information. Subsequently, under the guidance of the fused graph, the two underlying features are fused into high-level features, based on which clustering is conducted under the supervision of pseudo-labeling. Finally, the high-level features are uploaded to the server to refine the graph fusion and pseudo-labeling computation. Extensive experimental results demonstrate the effectiveness and superiority of FIMCFG. Our code is publicly available at https://github.com/PaddiHunter/FIMCFG.

Cross-modal alignment is an effective approach to improving visual classification. Existing studies typically enforce a one-step mapping that uses deep neural networks to project the visual features to mimic the distribution of textual features. However, they typically face difficulties in finding such a projection due to the two modalities in both the distribution of class-wise samples and the range of their feature values. To address this issue, this paper proposes a novel Semantic-Space-Intervened Diffusive Alignment method, termed SeDA, models a semantic space as a bridge in the visual-to-textual projection, considering both types of features share the same class-level information in classification. More importantly, a bi-stage diffusion framework is developed to enable the progressive alignment between the two modalities. Specifically, SeDA first employs a Diffusion-Controlled Semantic Learner to model the semantic feature space of visual features by constraining the interactive features of the diffusion model and the category centers of visual features. In the later stage of SeDA, the Diffusion-Controlled Semantic Translator focuses on learning the distribution of textual features from the semantic space. Meanwhile, the Progressive Feature Interaction Network introduces stepwise feature interactions at each alignment step, progressively integrating textual information into mapped features. Experimental results show that SeDA achieves stronger cross-modal feature alignment, leading to superior performance over existing methods across multiple scenarios.

Incomplete multi-view partial multi-label classification (IMvPMLC), which tackles the combined challenges of incompleteness in both multi-view and multi-label problems, has drawn considerable attention. Existing IMvPMLC methods have made progress but still face several challenges: (i) They mainly focus on the consistency of representations across multiple views but overlook the relationships among instances, leading to suboptimal representations. (ii) They primarily utilize only the available labels for supervised learning, ignoring the missing label distribution and limiting their ability to capture label correlations. In this paper, we propose a novel model named Dual Structure-guided Contrastive Network (DSCN) for IMvPMLC. Specifically, we introduce a similarity-guided instance-level contrastive learning mechanism to achieve multi-view consistent and discriminative representations across instances by leveraging instance structures, while a multi-view attention-based fusion strategy dynamically facilitates the fusion of multi-view representations to derive a robust consensus representation. Then, we design a multi-view shared classifier integrated with a correlation-guided label-level contrastive learning mechanism to enhance predictions by leveraging complementary information across multiple views and capturing label structures, effectively exploiting missing label distribution. Extensive experiments on five benchmark datasets demonstrate that, DSCN yields a more than 13% accuracy, compared with the state-of-the-art approaches. The code and datasets are available at https://anonymous.4open.science/r/DSCN-D471.

Multi-view clustering is an important approach to mining the valuable information within multi-view data. In this paper, we propose a novel multi-view deep subspace clustering method based on contrastive learning and Cauchy-Schwarz (CS) divergence. Our method not only uses contrastive learning techniques and block diagonalization constraints to guide representation matrix learning, but also combines representation learning and clustering processes to achieve the interaction of representation and clustering. First, we introduce a novel loss function based on CS divergence in the clustering module to achieve the interaction of representation and clustering. Second, we propose an extension of the multiple positive and negative pair diffusion method to enhance contrastive learning. Finally, we establish the equivalence between contrastive clustering and spectral clustering with orthogonal constraints, leading to a comprehensive model optimization. We evaluate our method on six publicly available datasets and compare its performance with eight competing methods. The results demonstrate the superiority of our method over the compared multi-view clustering methods.

Incomplete multi-view clustering focus on mining useful information from low-quality multiple sources, such as missing and distorted data that are prevalent in real life. However, after representation learning and the processing of incomplete information, existing methods often leave representations containing information task-irrelevant information. In addition, the separation between missing data imputation and clustering tasks leads to sub-optimal multi-view clustering performance. To address these issues, we propose an incomplete multi-view clustering method based on mutual information. For the problem of task-irrelevant information, we use incomplete view prediction to extract sufficient and minimal task-relevant information and provide theoretical proof from the perspective of mutual information. For the problem of separation between missing data imputation and clustering tasks, we integrate incomplete-view prediction with contrastive clustering, collaboratively enhancing the clustering performance. Comparative experiments on five public datasets, under both complete and incomplete scenarios, reveal that our method outperforms nine other competing approaches, demonstrating its effectiveness and robustness in handling multi-view data.

Incomplete multi-view clustering becomes an important research problem, since multi-view data with missing values are ubiquitous in real-world applications. Although great efforts have been made for incomplete multi-view clustering, there are still some challenges: 1) most existing methods didn't make full use of multi-view information to deal with missing values; 2) most methods just employ the consistent information within multi-view data but ignore the complementary information; 3) For the existing incomplete multi-view clustering methods, incomplete multi-view representation learning and clustering are treated as independent processes, which leads to performance gap. In this work, we proposed a novel Incomplete Contrastive Multi-View Clustering method with high-confidence guiding (ICMVC). Firstly, we proposed a multi-view consistency relation transfer plus graph convolutional network to tackle missing values problem. Secondly, instance-level attention fusion and high-confidence guiding are proposed to exploit the complementary information while instance-level contrastive learning for latent representation is designed to employ the consistent information. Thirdly, an end-to-end framework is proposed to integrate multi-view missing values handling, multi-view representation learning and clustering assignment for joint optimization. Experiments compared with state-of-the-art approaches demonstrated the effectiveness and superiority of our method. Our code is publicly available at https://github.com/liunian-Jay/ICMVC. The version with supplementary material can be found at http://arxiv.org/abs/2312.08697.

As a branch of clustering, multi-view clustering has received much attention in recent years. In practical applications, a common phenomenon is that partial views of some samples may be missing in the collected multi-view data, which poses a severe challenge to design the multi-view learning model and explore complementary and consistent information. Currently, most of the incomplete multi-view clustering methods only focus on exploring the information of available views while few works study the missing view recovery for incomplete multi-view learning. To this end, we propose an innovative diffusion-based missing view generation (DMVG) network. Moreover, for the scenarios with high missing rates, we further propose an incomplete multi-view data augmentation strategy to enhance the recovery quality for the missing views. Extensive experimental results show that the proposed DMVG can not only accurately predict missing views, but also further enhance the subsequent clustering performance in comparison with several state-of-the-art incomplete multi-view clustering methods.

Multi-view representation learning aims to extract comprehensive information from multiple sources. It has achieved significant success in applications such as video understanding and 3D rendering. However, how to improve the robustness and generalization of multi-view representations from unsupervised and incomplete scenarios remains an open question in this field. In this study, we discovered a positive correlation between the semantic distance of multi-view representations and the tolerance for data corruption. Moreover, we found that the information ratio of consistency and complementarity significantly impacts the performance of discriminative and generative tasks related to multi-view representations. Based on these observations, we propose an end-to-end CLustering-guided cOntrastiVE fusioN (CLOVEN) method, which enhances the robustness and generalization of multi-view representations simultaneously. To balance consistency and complementarity, we design an asymmetric contrastive fusion module. The module first combines all view-specific representations into a comprehensive representation through a scaling fusion layer. Then, the information of the comprehensive representation and view-specific representations is aligned via contrastive learning loss function, resulting in a view-common representation that includes both consistent and complementary information. We prevent the module from learning suboptimal solutions by not allowing information alignment between view-specific representations. We design a clustering-guided module that encourages the aggregation of semantically similar views. This action reduces the semantic distance of the view-common representation. We quantitatively and qualitatively evaluate CLOVEN on five datasets, demonstrating its superiority over 13 other competitive multi-view learning methods in terms of clustering and classification performance. In the data-corrupted scenario, our proposed method resists noise interference better than competitors. Additionally, the visualization demonstrates that CLOVEN succeeds in preserving the intrinsic structure of view-specific representations and improves the compactness of view-common representations. Our code can be found athttps://github.com/guanzhou-ke/cloven.

Multi-view (or -modality) representation learning aims to understand the relationships between different view representations. Existing methods disentangle multi-view representations into consistent and view-specific representations by introducing strong inductive biases, which can limit their generalization ability. In this paper, we propose a novel multi-view representation disentangling method that aims to go beyond inductive biases, ensuring both interpretability and generalizability of the resulting representations. Our method is based on the observation that discovering multi-view consistency in advance can determine the disentangling information boundary, leading to a decoupled learning objective. We also found that the consistency can be easily extracted by maximizing the transformation invariance and clustering consistency between views. These observations drive us to propose a two-stage framework. In the first stage, we obtain multi-view consistency by training a consistent encoder to produce semantically-consistent representations across views as well as their corresponding pseudo-labels. In the second stage, we disentangle specificity from comprehensive representations by minimizing the upper bound of mutual information between consistent and comprehensive representations. Finally, we reconstruct the original data by concatenating pseudo-labels and view-specific representations. Our experiments on four multi-view datasets demonstrate that our proposed method outperforms 12 comparison methods in terms of clustering and classification performance. The visualization results also show that the extracted consistency and specificity are compact and interpretable. Our code can be found at https://github.com/Guanzhou-Ke/DMRIB.

With the development of human-machine dialogue technology, more and more companies have launched their cognitive service products, such as Virtual Personal Assistant (VPA), smart speakers, shopping guide robots, etc. However, in these practical applications, most of the bots passively respond to user's utterances, lacking user preference knowledge and the proactive consciousness to lead the dialogue. Therefore, it is essential that bots proactively and naturally lead the dialogue from chitchat to service recommendation to meet user's requirements. To address this challenge, bots not only needs to detect the user's dialogue goal in real time, but also needs to plan a goal sequence based on user profile. In this paper, we propose DGPF, a Dialogue Goal Planning Framework. DGPF plans a reasonable goal sequence grounded on user's interests and personal KB before the conversation, additionally predicts user's true intent (i.e. dialogue goal) and judges whether the goal is completed based on the utterances during the conversation. DGPF includes a novel joint learning model that can simultaneously fix the two sub-tasks of goal completion estimation as well as current goal prediction, and improve each other's performance interactively. Our experimental results on the open dataset DuRecDial have been significantly improved compared to the baseline, which proves the effectiveness of our framework.

Maximum entropy discrimination (MED) is a general framework for discriminative estimation which integrates the principles of maximum entropy and maximum margin. In this paper, we propose a novel approach named multi-kernel MED (MKMED) for multi-view learning (MVL), which takes advantage of the comp lementary principle for MVL. Multiple kernels encode the similarities in different views. We obtain a kernel matrix by multiple kernel combination to make use of the complementary information in different views. Based on the kernel matrix obtained by multiple kernel combination, we can proceed MVL within the MED framework. The experimental results on multiple datasets demonstrate the effectiveness of the proposed MKMED. MKMED outperforms the single-view MEDs and a competing MVL mothod named SVM-2K, and is competitive with the state-of-the-art multi-view MED (MVMED) and even sometimes exceeds it.

Maximum entropy discrimination (MED) is a general framework for discriminative estimation based on maximum entropy and maximum margin principles, and can produce hard-margin support vector machines under some assumptions. Recently, the multiview version of MED multiview MED (MVMED) was proposed. In this paper, we try to explore a more natural MVMED framework by assuming two separate distributions p1( Θ1) over the first-view classifier parameter Θ1 and p2( Θ2) over the second-view classifier parameter Θ2 . We name the new MVMED framework as alternative MVMED (AMVMED), which enforces the posteriors of two view margins to be equal. The proposed AMVMED is more flexible than the existing MVMED, because compared with MVMED, which optimizes one relative entropy, AMVMED assigns one relative entropy term to each of the two views, thus incorporating a tradeoff between the two views. We give the detailed solving procedure, which can be divided into two steps. The first step is solving our optimization problem without considering the equal margin posteriors from two views, and then, in the second step, we consider the equal posteriors. Experimental results on multiple real-world data sets verify the effectiveness of the AMVMED, and comparisons with MVMED are also reported.