mlsanityData to 7 Oct 2026

Graph Neural Networks for Social Recommendation

arXiv 1902.07243 ↗First posted 19 Feb 2019cs.IR cs.LG cs.SIcited by 711

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Abstract (arXiv)

In recent years, Graph Neural Networks (GNNs), which can naturally integrate node information and topological structure, have been demonstrated to be powerful in learning on graph data. These advantages of GNNs provide great potential to advance social recommendation since data in social recommender systems can be represented as user-user social graph and user-item graph; and learning latent factors of users and items is the key. However, building social recommender systems based on GNNs faces challenges. For example, the user-item graph encodes both interactions and their associated opinions; social relations have heterogeneous strengths; users involve in two graphs (e.g., the user-user social graph and the user-item graph). To address the three aforementioned challenges simultaneously, in this paper, we present a novel graph neural network framework (GraphRec) for social recommendations. In particular, we provide a principled approach to jointly capture interactions and opinions in the user-item graph and propose the framework GraphRec, which coherently models two graphs and heterogeneous strengths. Extensive experiments on two real-world datasets demonstrate the effectiveness of the proposed framework GraphRec. Our code is available at urlhttps://github.com/wenqifan03/GraphRec-WWW19

Key facts. Cited by 711 papers in the map (data through 7 Oct 2026). First posted on arXiv 19 Feb 2019.

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Cited by year (2026 to 7 Oct)

Citing papers per year: 2021: 90; 2022: 138; 2023: 143; 2024: 155; 2025: 131; 2026: 54.

711 papers.

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  1. 2024A Survey on RAG Meeting LLMs: Towards Retrieval-Augmented Large Language Modelscited by 450
  2. 2021Identity-aware Graph Neural Networkscited by 197
  3. 2022Structure-Aware Transformer for Graph Representation Learningcited by 191
  4. 2023Recommender Systems in the Era of Large Language Models (LLMs)cited by 188
  5. 2021Is Homophily a Necessity for Graph Neural Networks?cited by 182
  6. 2021Are we really making much progress? Revisiting, benchmarking, and refining heterogeneous graph neural networkscited by 176
  7. 2021Anomaly Detection on Attributed Networks via Contrastive Self-Supervised Learningcited by 175
  8. 2021Equivariant Subgraph Aggregation Networkscited by 154
  9. 2021From Stars to Subgraphs: Uplifting Any GNN with Local Structure Awarenesscited by 154
  10. 2021Graph Condensation for Graph Neural Networkscited by 150
  11. 2022NAGphormer: A Tokenized Graph Transformer for Node Classification in Large Graphscited by 137
  12. 2023User Behavior Simulation with Large Language Model based Agentscited by 136
38 more
  1. 2024Graph-Mamba: Towards Long-Range Graph Sequence Modeling with Selective State Spacescited by 132
  2. 2021Interpreting and Unifying Graph Neural Networks with An Optimization Frameworkcited by 131
  3. 2023Dataset Distillation: A Comprehensive Reviewcited by 126
  4. 2022A Comprehensive Survey on Trustworthy Graph Neural Networks: Privacy, Robustness, Fairness, and Explainabilitycited by 118
  5. 2023Machine Learning for Synthetic Data Generation: A Reviewcited by 118
  6. 2024G-Designer: Architecting Multi-agent Communication Topologies via Graph Neural Networkscited by 110
  7. 2022Condensing Graphs via One-Step Gradient Matchingcited by 100
  8. 2021Dirichlet Energy Constrained Learning for Deep Graph Neural Networkscited by 97
  9. 2022Revisiting Over-smoothing and Over-squashing Using Ollivier-Ricci Curvaturecited by 96
  10. 2021Trustworthy AI: A Computational Perspectivecited by 93
  11. 2022Trustworthy Graph Neural Networks: Aspects, Methods and Trendscited by 88
  12. 2023Data Distillation: A Surveycited by 86
  13. 2024Graph Mamba: Towards Learning on Graphs with State Space Modelscited by 84
  14. 2022Graph Attention Multi-Layer Perceptroncited by 82
  15. 2021Few-Shot Graph Learning for Molecular Property Predictioncited by 81
  16. 2023Generative Diffusion Models on Graphs: Methods and Applicationscited by 79
  17. 2021AutoGCL: Automated Graph Contrastive Learning via Learnable View Generatorscited by 73
  18. 2021Robust Counterfactual Explanations on Graph Neural Networkscited by 72
  19. 2022Recommendation Unlearningcited by 71
  20. 2023Knowledge Graphs: Opportunities and Challengescited by 70
  21. 2022Knowledge Graph Contrastive Learning for Recommendationcited by 69
  22. 2022Rethinking and Scaling Up Graph Contrastive Learning: An Extremely Efficient Approach with Group Discriminationcited by 67
  23. 2022Empowering Graph Representation Learning with Test-Time Graph Transformationcited by 66
  24. 2024Dynamic Graph Representation with Knowledge-aware Attention for Histopathology Whole Slide Image Analysiscited by 66
  25. 2022A Survey of Graph Neural Networks for Social Recommender Systemscited by 65
  26. 2021Gophormer: Ego-Graph Transformer for Node Classificationcited by 63
  27. 2022Improving Fairness in Graph Neural Networks via Mitigating Sensitive Attribute Leakagecited by 61
  28. 2023Graph Neural Networks for temporal graphs: State of the art, open challenges, and opportunitiescited by 60
  29. 2021A Survey on Accuracy-oriented Neural Recommendation: From Collaborative Filtering to Information-rich Recommendationcited by 59
  30. 2021Graph Learning based Recommender Systems: A Reviewcited by 59
  31. 2022Generalization Analysis of Message Passing Neural Networks on Large Random Graphscited by 58
  32. 2024A Comprehensive Survey on Graph Reduction: Sparsification, Coarsening, and Condensationcited by 57
  33. 2021Federated Social Recommendation with Graph Neural Networkcited by 56
  34. 2025Knowledge Graph Retrieval-Augmented Generation for LLM-based Recommendationcited by 56
  35. 2021Graph Trend Filtering Networks for Recommendationscited by 53
  36. 2021Knowledge-aware Coupled Graph Neural Network for Social Recommendationcited by 49
  37. 2023GSLB: The Graph Structure Learning Benchmarkcited by 49
  38. 2023CoCo: A Coupled Contrastive Framework for Unsupervised Domain Adaptive Graph Classificationcited by 46

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