mlsanityData to 7 Oct 2026

Federated Social Recommendation with Graph Neural Network

arXiv 2111.10778 ↗First posted 21 Nov 2021cs.IR cs.LGcited by 56

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Abstract (arXiv)

Recommender systems have become prosperous nowadays, designed to predict users' potential interests in items by learning embeddings. Recent developments of the Graph Neural Networks~(GNNs) also provide recommender systems with powerful backbones to learn embeddings from a user-item graph. However, only leveraging the user-item interactions suffers from the cold-start issue due to the difficulty in data collection. Hence, current endeavors propose fusing social information with user-item interactions to alleviate it, which is the social recommendation problem. Existing work employs GNNs to aggregate both social links and user-item interactions simultaneously. However, they all require centralized storage of the social links and item interactions of users, which leads to privacy concerns. Additionally, according to strict privacy protection under General Data Protection Regulation, centralized data storage may not be feasible in the future, urging a decentralized framework of social recommendation. To this end, we devise a novel framework Fedrated Social recommendation with Graph neural network (FeSoG). Firstly, FeSoG adopts relational attention and aggregation to handle heterogeneity. Secondly, FeSoG infers user embeddings using local data to retain personalization. Last but not least, the proposed model employs pseudo-labeling techniques with item sampling to protect the privacy and enhance training. Extensive experiments on three real-world datasets justify the effectiveness of FeSoG in completing social recommendation and privacy protection. We are the first work proposing a federated learning framework for social recommendation to the best of our knowledge.

Key facts. Cited by 56 papers in the map (data through 7 Oct 2026). Builds on 39 of its 69 references that are in the map. First posted on arXiv 21 Nov 2021.

Builds on

39 of 69 references.

References · 39

  1. 2017Attention Is All You Needcited by 59,932
  2. 2016Semi-Supervised Classification with Graph Convolutional Networkscited by 11,282
  3. 2017Graph Attention Networkscited by 8,854
  4. 2016Communication-Efficient Learning of Deep Networks from Decentralized Datacited by 7,592
  5. 2017Inductive Representation Learning on Large Graphscited by 6,284
  6. 2019Advances and Open Problems in Federated Learningcited by 3,021
  7. 2016node2vec: Scalable Feature Learning for Networkscited by 1,913
  8. 2016Federated Learning: Strategies for Improving Communication Efficiencycited by 1,461
  9. 2018Graph Convolutional Neural Networks for Web-Scale Recommender Systemscited by 1,133
  10. 2019Graph Neural Networks for Social Recommendationcited by 711
  11. 2019Neural Graph Collaborative Filteringcited by 660
  12. 2020Heterogeneous Graph Transformercited by 581
27 more
  1. 2019Federated Machine Learning: Concept and Applicationscited by 409
  2. 2020Personalized Federated Learning: A Meta-Learning Approachcited by 309
  3. 2014RAPPOR: Randomized Aggregatable Privacy-Preserving Ordinal Responsecited by 262
  4. 2020Enhancing Graph Neural Network-based Fraud Detectors against Camouflaged Fraudsterscited by 248
  5. 2020KG-BART: Knowledge Graph-Augmented BART for Generative Commonsense Reasoningcited by 106
  6. 2019Secure Federated Matrix Factorizationcited by 102
  7. 2021FedGNN: Federated Graph Neural Network for Privacy-Preserving Recommendationcited by 97
  8. 2020Alleviating the Inconsistency Problem of Applying Graph Neural Network to Fraud Detectioncited by 81
  9. 2019Federated Collaborative Filtering for Privacy-Preserving Personalized Recommendation Systemcited by 79
  10. 2019Session-based Social Recommendation via Dynamic Graph Attention Networkscited by 76
  11. 2019A Neural Influence Diffusion Model for Social Recommendationcited by 69
  12. 2021Knowledge-Preserving Incremental Social Event Detection via Heterogeneous GNNscited by 51
  13. 2020DiffNet++: A Neural Influence and Interest Diffusion Network for Social Recommendationcited by 46
  14. 2021Augmenting Sequential Recommendation with Pseudo-Prior Items via Reversely Pre-training Transformercited by 45
  15. 2019Dual Graph Attention Networks for Deep Latent Representation of Multifaceted Social Effects in Recommender Systemscited by 41
  16. 2021ConsisRec: Enhancing GNN for Social Recommendation via Consistent Neighbor Aggregationcited by 39
  17. 2021Streaming Social Event Detection and Evolution Discovery in Heterogeneous Information Networkscited by 32
  18. 2020Federated Multi-view Matrix Factorization for Personalized Recommendationscited by 27
  19. 2020Composed Variational Natural Language Generation for Few-shot Intentscited by 19
  20. 2020BasConv: Aggregating Heterogeneous Interactions for Basket Recommendation with Graph Convolutional Neural Networkcited by 16
  21. 2021Pre-training Graph Neural Network for Cross Domain Recommendationcited by 14
  22. 2018SocialGCN: An Efficient Graph Convolutional Network based Model for Social Recommendationcited by 13
  23. 2020Federating Recommendations Using Differentially Private Prototypescited by 13
  24. 2020Characterizing Impacts of Heterogeneity in Federated Learning upon Large-Scale Smartphone Datacited by 9
  25. 2021From Intrinsic to Counterfactual: On the Explainability of Contextualized Recommender Systemscited by 9
  26. 2020Robust Federated Recommendation Systemcited by 8
  27. 2020Deoscillated Graph Collaborative Filteringcited by 2

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Cited by year (2026 to 7 Oct)

Citing papers per year: 2022: 12; 2023: 16; 2024: 15; 2025: 9; 2026: 4.

56 papers.

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  1. 2022A Comprehensive Survey on Trustworthy Graph Neural Networks: Privacy, Robustness, Fairness, and Explainabilitycited by 118
  2. 2022Trustworthy Graph Neural Networks: Aspects, Methods and Trendscited by 88
  3. 2022A Survey of Graph Neural Networks for Social Recommender Systemscited by 65
  4. 2022Personalized Subgraph Federated Learningcited by 54
  5. 2023FedFed: Feature Distillation against Data Heterogeneity in Federated Learningcited by 54
  6. 2022Federated Graph Neural Networks: Overview, Techniques and Challengescited by 52
  7. 2022GAP: Differentially Private Graph Neural Networks with Aggregation Perturbationcited by 46
  8. 2024FedGTA: Topology-aware Averaging for Federated Graph Learningcited by 44
  9. 2022Sequential Recommendation via Stochastic Self-Attentioncited by 37
  10. 2022A Survey on Federated Recommendation Systemscited by 25
  11. 2023A Survey on Privacy in Graph Neural Networks: Attacks, Preservation, and Applicationscited by 22
  12. 2024Rethinking Graph Backdoor Attacks: A Distribution-Preserving Perspectivecited by 22
38 more
  1. 2022Trustworthy Recommender Systemscited by 21
  2. 2023Dual Personalization on Federated Recommendationcited by 19
  3. 2023Federated Heterogeneous Graph Neural Network for Privacy-preserving Recommendationcited by 18
  4. 2023Data Scarcity in Recommendation Systems: A Surveycited by 17
  5. 2023Graph Collaborative Signals Denoising and Augmentation for Recommendationcited by 16
  6. 2022Federated Graph Machine Learning: A Survey of Concepts, Techniques, and Applicationscited by 14
  7. 2023FedACK: Federated Adversarial Contrastive Knowledge Distillation for Cross-Lingual and Cross-Model Social Bot Detectioncited by 12
  8. 2024Training Machine Learning models at the Edge: A Surveycited by 11
  9. 2024Safety in Graph Machine Learning: Threats and Safeguardscited by 11
  10. 2023Federated Recommendation with Additive Personalizationcited by 9
  11. 2023A Survey on Vulnerability of Federated Learning: A Learning Algorithm Perspectivecited by 9
  12. 2024Federated Graph Learning for Cross-Domain Recommendationcited by 9
  13. 2024Co-clustering for Federated Recommender Systemcited by 9
  14. 2023Turning Privacy-preserving Mechanisms against Federated Learningcited by 8
  15. 2024Unveiling Global Interactive Patterns across Graphs: Towards Interpretable Graph Neural Networkscited by 8
  16. 2024Mitigating Propensity Bias of Large Language Models for Recommender Systemscited by 8
  17. 2024Robustness Inspired Graph Backdoor Defensecited by 7
  18. 2025A Comprehensive Data-centric Overview of Federated Graph Learningcited by 7
  19. 2023Faithful and Consistent Graph Neural Network Explanations with Rationale Alignmentcited by 6
  20. 2022Cross-Network Social User Embedding with Hybrid Differential Privacy Guaranteescited by 5
  21. 2023Personalized Federated Domain Adaptation for Item-to-Item Recommendationcited by 4
  22. 2024HiFGL: A Hierarchical Framework for Cross-silo Cross-device Federated Graph Learningcited by 4
  23. 2022Sequential Recommendation with Auxiliary Item Relationships via Multi-Relational Transformercited by 3
  24. 2023Episodes Discovery Recommendation with Multi-Source Augmentationscited by 3
  25. 2023Decentralized Graph Neural Network for Privacy-Preserving Recommendationcited by 3
  26. 2024Do We Really Need Graph Convolution During Training? Light Post-Training Graph-ODE for Efficient Recommendationcited by 3
  27. 2024Privacy-preserving recommender system using the data collaboration analysis for distributed datasetscited by 2
  28. 2024P4GCN: Vertical Federated Social Recommendation with Privacy-Preserving Two-Party Graph Convolution Networkcited by 2
  29. 2024Cluster-Enhanced Federated Graph Neural Network for Recommendationcited by 2
  30. 2025Intellectual Property in Graph-Based Machine Learning as a Service: Attacks and Defensescited by 2
  31. 2023Label Inference Attacks against Node-level Vertical Federated GNNscited by 1
  32. 2024FedRKG: A Privacy-preserving Federated Recommendation Framework via Knowledge Graph Enhancementcited by 1
  33. 2025UFGraphFR: Graph Federation Recommendation System based on User Text description featurescited by 1
  34. 2025Personalized Recommendation Models in Federated Settings: A Surveycited by 1
  35. 2025Federated Latent Factor Learning for Recovering Wireless Sensor Networks Signal with Privacy-Preservingcited by 1
  36. 2023GNN4FR: A Lossless GNN-based Federated Recommendation Frameworkcited by 0
  37. 2025Far From Sight, Far From Mind: Inverse Distance Weighting for Graph Federated Recommendationcited by 0
  38. 2025High Order Collaboration-Oriented Federated Graph Neural Network for Accurate QoS Predictioncited by 0

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