Detecting anomalous nodes in continuously evolving graphs without labeled supervision requires representations that capture both local temporal context and globally consistent normal behavior—a combination that current methods do not jointly address. Existing dynamic anomaly detectors rely on localized temporal neighborhoods and cannot leverage globally similar normal patterns elsewhere in the graph, while existing retrieval-augmented graph methods either require labels or do not enforce strict temporal causality during retrieval. We propose DGRA-CL (Dynamic Graph Retrieval-Augmented Contrastive Learning), an unsupervised framework that learns discriminative temporal node representations for anomaly detection without labeled data. DGRA-CL transforms dynamic graphs into temporal sequences, employs time- and context-aware contrastive learning to learn normal node behavior patterns, retrieves similar normal exemplars from a training pool under a strict causality constraint, and fuses them via similarity-weighted aggregation to construct baseline representations. Anomalies are detected via deviation-based scoring measuring distance from these normal baselines. On four real-world dynamic graphs, DGRA-CL achieves statistically significant AUC gains of 1–2 points over the strongest baselines on three of four benchmarks (UCI Messages, Bitcoin-Alpha, Digg) and competitive performance on Reddit, while operating without anomaly labels and generalizing to unseen nodes.

Knowledge Tracing (KT) models aim to predict student performance from interaction histories in order to support personalised learning. However, many learners generate only limited interaction data, making reliable knowledge-state estimation difficult. Recent contrastive KT methods attempt to address this data sparsity through self-supervised representation learning from augmented versions of individual learner sequences, operating within an intra-learner paradigm, where contrastive signals are derived solely from variations of a single learner's trajectory. We advance prior work by proposing RACL (Retrieval-Augmented Contrastive Learning), a knowledge tracing framework that introduces an inter-learner contrastive paradigm, leveraging the observation that students with similar skill profiles often exhibit comparable learning trajectories. Cross-learner structure therefore provides naturally occurring positive and negative examples that are more pedagogically meaningful than synthetic augmentations. Experiments on four benchmarks demonstrate that RACL achieves +1.2% average AUC improvement over state-of-the-art methods, with 97% performance retention at 20% training data, indicating improved robustness under sparse-learning conditions.

Graph anomaly detection identifies abnormal patterns in networks but faces label scarcity and extreme class imbalance. While graph contrastive learning offers unsupervised solutions, existing methods suffer from two limitations: random augmentations break semantic consistency in positive pairs, while naive negative sampling produces trivial contrasts. We propose AC2L-GAD, an Active Counterfactual Contrastive Learning framework addressing both limitations through principled counterfactual reasoning. By combining information-theoretic active selection with counterfactual generation, our approach identifies structurally complex nodes and generates anomaly-preserving positive augmentations alongside hard negative contrasts, while restricting expensive counterfactual generation to a strategically selected subset. This design reduces computational overhead by approximately 65% compared to full-graph counterfactual generation while maintaining detection quality. Experiments on nine benchmark datasets, including real-world financial transaction graphs from GADBench, show that AC2L-GAD achieves competitive or superior performance compared to state-of-the-art baselines, with notable gains in datasets where anomalies exhibit complex attribute-structure interactions.

Despite its effectiveness, spectral clustering is often impractical for large-scale data due to its high computational complexity$(O(n^{2}))$and limited clustering quality arising from three fundamental limitations: (1) reliance on a fixed similarity graph that cannot adapt to complex local structures, (2) inability to capture higher-order relationships, and (3) a decoupled two-step pipeline that separates embedding and clustering. To address these issues, we propose OA2H-SP, a novel framework that achieves linear-time spectral clustering$(O(nm)$with$m\ll n)$while enhancing clustering accuracy and scalability. Our method constructs an anchor-adaptive hypergraph to model both adaptive and higher-order affinities efficiently. It further unifies representation learning and discrete clustering in a one-step optimization scheme, avoiding the need for k-means post-processing. Extensive experiments on benchmark datasets demonstrate that$\text{OA}^{2}\mathrm{H}$. SP delivers superior performance in terms of accuracy, robustness, and runtime compared to existing hypergraph-based and anchor-driven spectral clustering methods.

Attributed graph clustering is crucial for analyzing complex networks, but integrating heterogeneous structural and attribute information remains a challenging task. Existing methods often struggle to balance these aspects, resulting in suboptimal clustering performance. To address this, we propose DV-ERNMF (Dual-View Entropy Regularized Nonnegative Matrix Factorization), a framework that decomposes the attributed network into two complementary views, structure and attributes, for separate, yet coordinated modeling. In the structural view, we introduce a Symmetric Nonnegative Matrix Factorization (SNMF) model enhanced with entropy-based regularization to yield sharper cluster assignments. For the attribute view, we construct a clustering-specific similarity matrix via subspace learning and apply SNMF to extract a structurally consistent cluster pattern. A new adaptive entropy-based regularizer is applied to enforce consistency between the partitions obtained from both views. The entire model is optimized jointly using a multiplicative update rule with theoretical convergence guarantees. Experimental results on synthetic and real-world networks demonstrate that DV-ERNMF significantly outperforms state-of-the-art methods.

Multi-view classification (MVC) has emerged as a promising approach in machine learning, aimed at enhancing classification accuracy by leveraging information from multiple perspectives. As the demand for more robust, interpretable, and effective machine learning models grows, MVC has shown significant progress over the past decade, yet it faces new challenges. Despite extensive literature on this subject, there is a notable absence of a comprehensive synthesis of MVC methods. This article addresses this gap by presenting a thorough overview and classification of MVC methods, categorizing them into seven distinct classes: text, image, time series, hyperspectral, video, signal, and 3D shape. Our meticulous examination within each class highlights advancements and evaluates their applicability in both supervised and semi-supervised learning contexts. Beyond this retrospective analysis, we explore future directions for research and development in this domain. This survey serves as a compendium of existing knowledge and as a guide for future endeavors in MVC, shaping the trajectory of ongoing research and innovation.

Attributed graph clustering is a fundamental task in network mining, essential for uncovering valuable insights in various applications. However, the heterogeneity of information from structural and attribute spaces poses significant challenges in achieving consistent and meaningful clustering. To address this, we propose Relative Entropy-based Regularized Non-negative Matrix Factorization (RENMF), a novel approach that integrates structural and attribute information through advanced matrix factorization techniques. RENMF employs Symmetric NMF and Projective NMF to extract community membership distributions from the structural and attribute spaces, respectively. By treating these distributions as homogeneous, RENMF preserves distinct, denoised information from both spaces while considering their heterogeneous complementary information. We introduce Relative Entropy (RE) as a novel regularization term to facilitate interaction between these spaces, aiming to maximize consistency between the discovered latent distributions. In this interaction, we leverage the asymmetric property of RE to emphasize attributes as essential complementary information for structural clustering. The RENMF model is solved using a new iterative multiplicative update rule, with convergence theoretically proven. We evaluate RENMF’s effectiveness through extensive experiments on 10 real-world networks, comparing it to 11 state-of-the-art clustering methods. The results demonstrate RENMF’s superiority in ground truth matching and key quality metrics, outperforming existing methods.

In recent years, the surge in social media platform usage has sparked a heightened interest in applying recommender systems (RSs) within the food industry. Traditionally, the exploration of user interests focused on analyzing behaviors linked to food selection. The availability of user interaction datasets now provides avenues for deeper insights into food content and intricate user relationships. This paper advocates strategically integrating Heterogeneous Information Networks (HIN) into recommender system frameworks. It introduces the Heterogeneous Attention Network-based User Interests-Aware Food Recommender System (UIFRS-HAN), designed for personalized food recommendations. By leveraging HIN and a two-step attention mechanism, UIFRS-HAN captures diverse entities and relationships within a unified framework. UIFRS-HAN employs an attention technique to reconstruct node features and edges, incorporating a dual hierarchical attention mechanism for improved unsupervised learning of attributed graph representations. Besides, HIN allows the model to uncover meaningful relationships between nodes, particularly when directed relationships are unclear. Through a defined meta-path-based attention mechanism, UIFRS-HAN generates diverse recommendations based on users’ interests across various relations among different types of nodes of the HIN. By discerning intricate patterns and correlations, UIFRS-HAN surpasses traditional approaches in delivering refined and contextually relevant recommendations. The proposed model enhances representation depth and accuracy by employing node embedding through a hierarchical meta-path structure. Rigorous testing on Allrecipes.com and Food.com datasets, compared against 15 baselines and state-of-the-art models, confirms the technical soundness and superiority of UIFRS-HAN in providing precise and personalized food recommendations. • A novel food recommender system based on a heterogeneous attention network. • The dual attention method is used to learn the meta-path in HIN. • Employing unsupervised learning based on hierarchical attention in the HIN. • The experiment method involves two real datasets based on heterogeneous graphs.

Attributed graph clustering is a prominent research area, catering to the increasing need for understanding real-world systems by uncovering exhaustive meaningful latent knowledge from heterogeneous spaces. Therefore, the critical challenge of this problem is the strategy used to extract and integrate meaningful heterogeneous information from structure and attribute sources. To this end, in this paper, we propose a novel Nonnegative Matrix Factorization (NMF)-based model for attributed graph clustering. In this method, firstly, we filter structure and attribute spaces from noise and irrelevant information for clustering by applying Symmetric NMF and NMF during the clustering task, respectively. Then, to overcome the heterogeneity of discovered partitions from spaces, we suggest a new regularization term to inject the complementary information from the attribute partition into the structure by transforming them into their pairwise similarity spaces, which are homogeneous. Simultaneously, by setting orthogonality constraints on the discovered communities, we encourage the representation of distinct and non-overlapping communities within the attributed graph. Finally, we collect all these terms in a unified framework to learn a meaningful partition containing consensus and complementary information from structure and attributes. Then a new iterative multiplicative updating strategy is proposed to solve the proposed model, and its convergence is proven theoretically. Our experiments on the nine popular real-world networks illustrate the supremacy of our methods among eleven widely recognized and stat-of-the-arts attributed graph clustering methods in terms of accurately matching the ground truth and quality-based metrics.

Existing food recommendation models have typically suggested foods or recipes to single users. However, in reality, users may be members of a group, family, or community, requiring food recommendation systems to support the whole group. Food recommendations to groups are a more challenging task than food recommendations to individuals, as each person’s preferences in the group should be addressed before giving the recommendations. Suggesting healthy food is also important in a food recommendation system, given that unhealthy diets can lead to different diseases. To address these challenges, a new healthy group food recommendation system based on deep social community detection and user popularity is developed in this study. To this end, an innovative deep community detection approach based on feature learning and deep neural networks is developed using the calculated time-aware user similarity measure. In addition, a health-aware rate prediction measurement, which considers both group preferences and health factors, is developed. Different experiments are designed on two real-food social networks to specify the efficiency of the suggested model, and the results indicate that it enhanced the single-user and group satisfaction metrics.

Network clustering is one of the fundamental unsupervised methods of knowledge discovery. Its goal is to group similar nodes together without supervision or prior knowledge of the nature of the clusters. Among various clustering methods, semi-supervised clustering detection is one of the most promising approaches for community detection because of its ability to employ side information to better understand network topology. However, most of the previous work faces two problems: the use of linear methods to reduce dimensionality and the random selection of side information, and as a result of these two drawbacks, semi-supervised community detection methods are less efficient. To fill these gaps, we developed an end-to-end deep semi-supervisor community detection (DSSC) for complex networks. A new learning objective is designed that uses a semi-autoencoder (SeAE) with a defined pair-wise constraint matrix based on point-wise mutual information (PMI) in the representation layer to accurately learn distinctive features and, in the clustering layer, adds a pair-wise constraint as a term to minimize distance within the cluster while the distance between clusters increases. The results show that our method performs unexpectedly well in comparison to the existing state-of-the-art community detection methods in complex networks.

Attributed graph clustering aims to group nodes into disjoint categories using deep learning to represent node embeddings and has shown promising performance across various applications. However, two main challenges hinder further performance improvement. Firstly, reliance on unsupervised methods impedes the learning of low-dimensional, clustering-specific features in the representation layer, thus impacting clustering performance. Secondly, the predominant use of separate approaches leads to suboptimal learned embeddings that are insufficient for subsequent clustering steps. To address these limitations, we propose a novel method called Semi-supervised Deep Attributed Clustering using Dual Autoencoder (SDAC-DA). This approach enables semi-supervised deep end-to-end clustering in attributed networks, promoting high structural cohesiveness and attribute homogeneity. SDAC-DA transforms the attribute network into a dual-view network, applies a semi-supervised autoencoder layering approach to each view, and integrates dimensionality reduction matrices by considering complementary views. The resulting representation layer contains high clustering-friendly embeddings, which are optimized through a unified end-to-end clustering process for effectively identifying clusters. Extensive experiments on both synthetic and real networks demonstrate the superiority of our proposed method over seven state-of-the-art approaches.

Abstract Attributed graph clustering, the task of grouping nodes into communities using both graph structure and node attributes, is a fundamental problem in graph analysis. Recent approaches have utilized deep learning for node embedding followed by conventional clustering methods. However, these methods often suffer from the limitations of relying on the original network structure, which may be inadequate for clustering due to sparsity and noise, and using separate approaches that yield suboptimal embeddings for clustering. To address these limitations, we propose a novel method called Deep Attributed Clustering with High-order Proximity Preserve (DAC-HPP) for attributed graph clustering. DAC-HPP leverages an end-to-end deep clustering framework that integrates high-order proximities and fosters structural cohesiveness and attribute homogeneity. We introduce a modified Random Walk with Restart that captures k-order structural and attribute information, enabling the modelling of interactions between network structure and high-order proximities. A consensus matrix representation is constructed by combining diverse proximity measures, and a deep joint clustering approach is employed to leverage the complementary strengths of embedding and clustering. In summary, DAC-HPP offers a unique solution for attributed graph clustering by incorporating high-order proximities and employing an end-to-end deep clustering framework. Extensive experiments demonstrate its effectiveness, showcasing its superiority over existing methods. Evaluation on synthetic and real networks demonstrates that DAC-HPP outperforms seven state-of-the-art approaches, confirming its potential for advancing attributed graph clustering research.

In recent decades, the improvement of computer technology has increased the growth of high-dimensional microarray data. Thus, data mining methods for DNA microarray data classification usually involve samples consisting of thousands of genes. One of the efficient strategies to solve this problem is gene selection, which improves the accuracy of microarray data classification and also decreases computational complexity. In this paper, a novel social network analysis-based gene selection approach is proposed. The proposed method has two main objectives of the relevance maximization and redundancy minimization of the selected genes. In this method, on each iteration, a maximum community is selected repetitively. Then among the existing genes in this community, the appropriate genes are selected by using the node centrality-based criterion. The reported results indicate that the developed gene selection algorithm while increasing the classification accuracy of microarray data, will also decrease the time complexity.

Gene expression data have become increasingly important in machine learning and computational biology over the past few years. In the field of gene expression analysis, several matrix factorization-based dimensionality reduction methods have been developed. However, such methods can still be improved in terms of efficiency and reliability. In this paper, an innovative approach to feature selection, called Dual Regularized Unsupervised Feature Selection Based on Matrix Factorization and Minimum Redundancy (DR-FS-MFMR), is introduced. The major focus of DR-FS-MFMR is to discard redundant features from the set of original features. In order to reach this target, the primary feature selection problem is defined in terms of two aspects: (1) the matrix factorization of data matrix in terms of the feature weight matrix and the representation matrix, and (2) the correlation information related to the selected features set. Then, the objective function is enriched by employing two data representation characteristics along with an inner product regularization criterion to perform both the redundancy minimization process and the sparsity task more precisely. To demonstrate the proficiency of the DR-FS-MFMR method, a large number of experimental studies are conducted on nine gene expression datasets. The obtained computational results indicate the efficiency and productivity of DR-FS-MFMR for the gene selection task.

As the community detection is able to facilitate the discovery of hidden information in complex networks, it has been drawn a lot of attention recently. However, due to the growth in computational power and data storage, the scale of these complex networks has grown dramatically. In order to detect communities by utilizing global approaches, it is required to have all the global information of the whole network; something which is impossible, because of the rapid growth in the size of the networks. In this paper, a local approach has been proposed based on the detection and expansion of core nodes. First, a community's central node (core node) which has a high level of embeddedness is detected based on the similarity between graph's nodes. By using this, the total weights of a weighted graph's edges created. Following by that, the expansion of these nodes will be considered, by utilizing the concept of node's membership based on the definition of strong community for weighted graphs. It can be seen that in detecting communities, the more accurate the weights of edges detected based on the node similarity, the more precise the local algorithm will be. In fact, the algorithm has the ability to detect all the graph's communities in a network using local information as well as identifying various roles of nodes, either being (core or outlier). Test results on both real-world and artificial networks prove that the quality of the communities which are detected by the proposed algorithm is better than the results which are achieved by other state-of-the-art algorithms in the complex networks.