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.

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.

Recommender systems play a critical role in digital platforms by providing personalized suggestions based on user preferences and interactions. While Graph Neural Networks (GNNs) have improved recommendation accuracy by modeling complex user-item relationships, existing systems often struggle to integrate heterogeneous data sources, including textual reviews, item attributes, and user profiles, into a unified and explainable framework. To address these limitations, we present ExpLMGCN, an explainable multi-modal recommender system that fuses user-item interactions, review embeddings, item features, and user profiles using GNNs and pre-trained language models. Fine-grained user preferences are captured by extracting opinion-aspect pairs (OAs) from reviews and representing them as User–OA and Item–OA bipartite graphs. A contrastive learning framework aligns multi-modal embeddings into a shared latent space, while explanations are generated by ranking OAs based on preference similarity, sentiment, novelty, and factuality. Multiple candidate explanations are produced via large language models, and the optimal one is selected using semantic alignment, multi-aspect coverage, and a self-consistency feedback loop. Extensive experiments on benchmark datasets demonstrate that ExpLMGCN consistently outperforms state-of-the-art baselines, achieving up to 4.39% improvement in recommendation accuracy across Recall and NDCG metrics. Moreover, ExpLMGCN substantially enhances explanation quality, yielding notable gains across BERTScore, ROUGE, and SBERT, and achieving up to 29.4% improvement in BLEU score. These results highlight the model’s ability to provide not only more accurate recommendations but also more coherent and semantically aligned explanations.

In this paper, we propose a novel framework to significantly enhance the inference speed and memory efficiency of Hypergraph Neural Networks (HGNNs) while preserving their high accuracy. Our approach utilizes an advanced teacher-student knowledge distillation strategy. The teacher model, consisting of an HGNN and a Multi-Layer Perceptron (MLP), not only produces soft labels but also transfers structural and high-order information to a lightweight Graph Convolutional Network (GCN) known as TinyGCN. This dual transfer mechanism enables the student model to effectively capture complex dependencies while benefiting from the faster inference and lower computational cost of the lightweight GCN. The student model is trained using both labeled data and soft labels provided by the teacher, with contrastive learning further ensuring that the student retains high-order relationships. This makes the proposed method efficient and suitable for real-time applications, achieving performance comparable to traditional HGNNs but with significantly reduced resource requirements.

This paper proposes SHARP-Distill (\textbf{S}peedy \textbf{H}ypergraph \textbf{A}nd \textbf{R}eview-based \textbf{P}ersonalised \textbf{Distill}ation), a novel knowledge distillation approach based on the teacher-student framework that combines Hypergraph Neural Networks (HGNNs) with language models to enhance recommendation quality while significantly improving inference time. The teacher model leverages HGNNs to generate user and item embeddings from interaction data, capturing high-order and group relationships, and employing a pre-trained language model to extract rich semantic features from textual reviews. We utilize a contrastive learning mechanism to ensure structural consistency between various representations. The student includes a shallow and lightweight GCN called CompactGCN designed to inherit high-order relationships while reducing computational complexity. Extensive experiments on real-world datasets demonstrate that SHARP-Distill achieves up to 68× faster inference time compared to HGNN and 40× faster than LightGCN while maintaining competitive recommendation accuracy.

Recommender systems leveraging deep learning have significantly improved personalised item suggestions, yet single-criteria models often overlook the nuanced nature of user preferences. Multi-Criteria Recommender Systems (MCRS) address this by modeling multiple aspects (e.g., taste, appearance, location). However, existing deep learning approaches—especially those using shared embeddings or matrix factorization—struggle to capture complex structural and cross-criteria dependencies. To overcome these challenges, we propose a novel framework, D-MGAC (Dual Multiview Graph Attention and Contrastive learning), which formulates MCRS as a multi-edge bipartite graph and applies multiview dual graph attention mechanisms to model both local (per-criterion) and global (cross-criteria) interactions. Furthermore, we define anchor-based contrastive learning in both local and global views to refine the representation quality. Experiments on Yahoo!Movies and BeerAdvocate datasets demonstrate D-MGAC’s superiority, outperforming recent state-of-the-art models.

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.

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.

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.

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.