mlsanityData to 14 Oct 2026

Knowledge-aware Coupled Graph Neural Network for Social Recommendation

arXiv 2110.03987 ↗First posted 8 Oct 2021cs.IR cs.AIcited by 49

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Abstract (arXiv)

Social recommendation task aims to predict users' preferences over items with the incorporation of social connections among users, so as to alleviate the sparse issue of collaborative filtering. While many recent efforts show the effectiveness of neural network-based social recommender systems, several important challenges have not been well addressed yet: (i) The majority of models only consider users' social connections, while ignoring the inter-dependent knowledge across items; (ii) Most of existing solutions are designed for singular type of user-item interactions, making them infeasible to capture the interaction heterogeneity; (iii) The dynamic nature of user-item interactions has been less explored in many social-aware recommendation techniques. To tackle the above challenges, this work proposes a Knowledge-aware Coupled Graph Neural Network (KCGN) that jointly injects the inter-dependent knowledge across items and users into the recommendation framework. KCGN enables the high-order user- and item-wise relation encoding by exploiting the mutual information for global graph structure awareness. Additionally, we further augment KCGN with the capability of capturing dynamic multi-typed user-item interactive patterns. Experimental studies on real-world datasets show the effectiveness of our method against many strong baselines in a variety of settings. Source codes are available at: https://github.com/xhcdream/KCGN.

Key facts. Cited by 49 papers in the map (data through 14 Oct 2026). Builds on 17 of its 33 references that are in the map. First posted on arXiv 8 Oct 2021.

Builds on

17 of 33 references.

References · 17

  1. 2017Attention Is All You Needcited by 60,361
  2. 2020LightGCN: Simplifying and Powering Graph Convolution Network for Recommendationcited by 1,139
  3. 2018Graph Convolutional Neural Networks for Web-Scale Recommender Systemscited by 1,138
  4. 2018Deep Graph Infomaxcited by 1,106
  5. 2019BERT4Rec: Sequential Recommendation with Bidirectional Encoder Representations from Transformercited by 964
  6. 2019Graph Neural Networks for Social Recommendationcited by 714
  7. 2019Neural Graph Collaborative Filteringcited by 661
  8. 2019KGAT: Knowledge Graph Attention Network for Recommendationcited by 343
  9. 2019Position-aware Graph Neural Networkscited by 203
  10. 2020Revisiting Graph based Collaborative Filtering: A Linear Residual Graph Convolutional Network Approachcited by 120
  11. 2019Knowledge-aware Graph Neural Networks with Label Smoothness Regularization for Recommender Systemscited by 107
  12. 2019Hierarchical Gating Networks for Sequential Recommendationcited by 101
5 more
  1. 2019Representation Learning for Attributed Multiplex Heterogeneous Networkcited by 91
  2. 2019Session-based Social Recommendation via Dynamic Graph Attention Networkscited by 76
  3. 2019A Neural Influence Diffusion Model for Social Recommendationcited by 70
  4. 2019Dual Graph Attention Networks for Deep Latent Representation of Multifaceted Social Effects in Recommender Systemscited by 41
  5. 2019Relational Collaborative Filtering:Modeling Multiple Item Relations for Recommendationcited by 23

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Cited by year (2026 to 14 Oct)

Citing papers per year: 2021: 2; 2022: 17; 2023: 10; 2024: 11; 2025: 5; 2026: 4.

49 papers.

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  1. 2022Knowledge Graph Contrastive Learning for Recommendationcited by 70
  2. 2022A Survey on Graph Representation Learning Methodscited by 66
  3. 2022Spatial-Temporal Hypergraph Self-Supervised Learning for Crime Predictioncited by 39
  4. 2022Contrastive Meta Learning with Behavior Multiplicity for Recommendationcited by 37
  5. 2024GITA: Graph to Visual and Textual Integration for Vision-Language Graph Reasoningcited by 33
  6. 2022Self-Supervised Hypergraph Transformer for Recommender Systemscited by 32
  7. 2022Unlearning Graph Classifiers with Limited Data Resourcescited by 31
  8. 2022Multiplex Heterogeneous Graph Convolutional Networkcited by 27
  9. 2024STG-Mamba: Spatial-Temporal Graph Learning via Selective State Space Modelcited by 24
  10. 2022GREASE: Generate Factual and Counterfactual Explanations for GNN-based Recommendationscited by 22
  11. 2022Multi-Behavior Sequential Recommendation with Temporal Graph Transformercited by 20
  12. 2024RecDiff: Diffusion Model for Social Recommendationcited by 20
37 more
  1. 2023Machine Unlearning: A Surveycited by 19
  2. 2021Self-supervised Contrastive Attributed Graph Clusteringcited by 18
  3. 2023SSLRec: A Self-Supervised Learning Framework for Recommendationcited by 17
  4. 2022Shilling Black-box Recommender Systems by Learning to Generate Fake User Profilescited by 15
  5. 2023Recent Developments in Recommender Systems: A Surveycited by 14
  6. 2022MGDCF: Distance Learning via Markov Graph Diffusion for Neural Collaborative Filteringcited by 11
  7. 2022On the Probability of Necessity and Sufficiency of Explaining Graph Neural Networks: A Lower Bound Optimization Approachcited by 11
  8. 2023TransGNN: Harnessing the Collaborative Power of Transformers and Graph Neural Networks for Recommender Systemscited by 11
  9. 2023KGTrust: Evaluating Trustworthiness of SIoT via Knowledge Enhanced Graph Neural Networkscited by 9
  10. 2024Coupling Graph Neural Networks with Fractional Order Continuous Dynamics: A Robustness Studycited by 9
  11. 2023Hierarchical Contrastive Learning Enhanced Heterogeneous Graph Neural Networkcited by 6
  12. 2023Instant Representation Learning for Recommendation over Large Dynamic Graphscited by 6
  13. 2024A Comprehensive Survey on Self-Supervised Learning for Recommendationcited by 6
  14. 2024Generalization of Geometric Graph Neural Networks with Lipschitz Loss Functionscited by 6
  15. 2024Node Duplication Improves Cold-start Link Predictioncited by 4
  16. 2026DynamicGTR: Leveraging Graph Topology Representation Preferences to Boost VLM Capabilities on Graph QAscited by 4
  17. 2023Decentralized Graph Neural Network for Privacy-Preserving Recommendationcited by 3
  18. 2025Safeguarding Graph Neural Networks against Topology Inference Attackscited by 3
  19. 2021Learning to Detect Critical Nodes in Sparse Graphs via Feature Importance Awarenesscited by 2
  20. 2022Collaborative Reflection-Augmented Autoencoder Network for Recommender Systemscited by 2
  21. 2022Faithful Explanations for Deep Graph Modelscited by 2
  22. 2024Balancing User Preferences by Social Networks: A Condition-Guided Social Recommendation Model for Mitigating Popularity Biascited by 2
  23. 2022TeKo: Text-Rich Graph Neural Networks with External Knowledgecited by 1
  24. 2022DFG-NAS: Deep and Flexible Graph Neural Architecture Searchcited by 1
  25. 2024Advancing Graph Neural Networks with HL-HGAT: A Hodge-Laplacian and Attention Mechanism Approach for Heterogeneous Graph-Structured Datacited by 1
  26. 2025Handling Heterophily in Recommender Systems with Wavelet Hypergraph Diffusioncited by 1
  27. 2022Gender In Gender Out: A Closer Look at User Attributes in Context-Aware Recommendationcited by 0
  28. 2023Adaptive Depth Graph Attention Networkscited by 0
  29. 2023STUDY: Socially Aware Temporally Causal Decoder Recommender Systemscited by 0
  30. 2024Stability of Explainable Recommendationcited by 0
  31. 2024Robust Explainable Recommendationcited by 0
  32. 2025Hypergraph Diffusion for High-Order Recommender Systemscited by 0
  33. 2025Burger: Robust Graph Denoising-augmentation Fusion and Multi-semantic Modeling in Social Recommendationcited by 0
  34. 2025Efficient Knowledge Tracing Leveraging Higher-Order Information in Integrated Graphscited by 0
  35. 2026Graph Negative Feedback Bias Correction Framework for Adaptive Heterophily Modelingcited by 0
  36. 2026AgentCAT: Simulating Computerized Adaptive Testing via Multi-Agent Large Language Modelscited by 0
  37. 2026Knowledge Graphs Meet Graph Neural Networks: A Comprehensive Surveycited by 0