mlsanityData to 13 Oct 2026

Compositional Fairness Constraints for Graph Embeddings

arXiv 1905.10674 ↗First posted 25 May 2019cs.LG cs.AI stat.MLcited by 126

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Abstract (arXiv)

Learning high-quality node embeddings is a key building block for machine learning models that operate on graph data, such as social networks and recommender systems. However, existing graph embedding techniques are unable to cope with fairness constraints, e.g., ensuring that the learned representations do not correlate with certain attributes, such as age or gender. Here, we introduce an adversarial framework to enforce fairness constraints on graph embeddings. Our approach is compositional---meaning that it can flexibly accommodate different combinations of fairness constraints during inference. For instance, in the context of social recommendations, our framework would allow one user to request that their recommendations are invariant to both their age and gender, while also allowing another user to request invariance to just their age. Experiments on standard knowledge graph and recommender system benchmarks highlight the utility of our proposed framework.

Key facts. Cited by 126 papers in the map (data through 13 Oct 2026). First posted on arXiv 25 May 2019.

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Cited by year (2026 to 13 Oct)

Citing papers per year: 2021: 18; 2022: 33; 2023: 40; 2024: 19; 2025: 12; 2026: 4.

126 papers.

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  1. 2021Towards a Unified Framework for Fair and Stable Graph Representation Learningcited by 128
  2. 2022A Comprehensive Survey on Trustworthy Graph Neural Networks: Privacy, Robustness, Fairness, and Explainabilitycited by 118
  3. 2021Trustworthy AI: A Computational Perspectivecited by 93
  4. 2021EDITS: Modeling and Mitigating Data Bias for Graph Neural Networkscited by 92
  5. 2022Trustworthy Graph Neural Networks: Aspects, Methods and Trendscited by 88
  6. 2022Fairness in Graph Mining: A Surveycited by 76
  7. 2021Subgroup Generalization and Fairness of Graph Neural Networkscited by 69
  8. 2021FairDrop: Biased Edge Dropout for Enhancing Fairness in Graph Representation Learningcited by 68
  9. 2022Improving Fairness in Graph Neural Networks via Mitigating Sensitive Attribute Leakagecited by 61
  10. 2022Learning Fair Node Representations with Graph Counterfactual Fairnesscited by 58
  11. 2023A Survey on Intersectional Fairness in Machine Learning: Notions, Mitigation, and Challengescited by 41
  12. 2021Personalized Counterfactual Fairness in Recommendationcited by 38
38 more
  1. 2022Representation Bias in Data: A Survey on Identification and Resolution Techniquescited by 37
  2. 2022FMP: Toward Fair Graph Message Passing against Topology Biascited by 35
  3. 2021CrossWalk: Fairness-enhanced Node Representation Learningcited by 34
  4. 2022A Comprehensive Survey on Trustworthy Recommender Systemscited by 32
  5. 2021TFROM: A Two-sided Fairness-Aware Recommendation Model for Both Customers and Providerscited by 31
  6. 2023Inductive Graph Unlearningcited by 31
  7. 2021Fair Representation Learning for Heterogeneous Information Networkscited by 28
  8. 2021Unbiased Graph Embedding with Biased Graph Observationscited by 28
  9. 2023On Generalized Degree Fairness in Graph Neural Networkscited by 28
  10. 2022Fair Node Representation Learning via Adaptive Data Augmentationcited by 27
  11. 2022RawlsGCN: Towards Rawlsian Difference Principle on Graph Convolutional Networkcited by 27
  12. 2022Fairness in Recommendation: Foundations, Methods and Applicationscited by 27
  13. 2021A Sociotechnical View of Algorithmic Fairnesscited by 24
  14. 2022FairVFL: A Fair Vertical Federated Learning Framework with Contrastive Adversarial Learningcited by 23
  15. 2022Joint Multisided Exposure Fairness for Recommendationcited by 22
  16. 2023Fair Attribute Completion on Graph with Missing Attributescited by 22
  17. 2023Counterfactual Learning on Graphs: A Surveycited by 22
  18. 2023Fairness-Aware Graph Neural Networks: A Surveycited by 22
  19. 2022Group Property Inference Attacks Against Graph Neural Networkscited by 21
  20. 2022Fairness Amidst Non-IID Graph Data: A Literature Reviewcited by 20
  21. 2022A Survey on Fairness for Machine Learning on Graphscited by 20
  22. 2024FairSIN: Achieving Fairness in Graph Neural Networks through Sensitive Information Neutralizationcited by 20
  23. 2023Improving Recommendation Fairness via Data Augmentationcited by 18
  24. 2021The KL-Divergence between a Graph Model and its Fair I-Projection as a Fairness Regularizercited by 17
  25. 2021Modeling Techniques for Machine Learning Fairness: A Surveycited by 17
  26. 2022CausPref: Causal Preference Learning for Out-of-Distribution Recommendationcited by 17
  27. 2023The Devil is in the Data: Learning Fair Graph Neural Networks via Partial Knowledge Distillationcited by 16
  28. 2021InfoFair: Information-Theoretic Intersectional Fairnesscited by 15
  29. 2022FairEGM: Fair Link Prediction and Recommendation via Emulated Graph Modificationcited by 15
  30. 2023A Survey of Imbalanced Learning on Graphs: Problems, Techniques, and Future Directionscited by 15
  31. 2024Fair Graph Representation Learning via Sensitive Attribute Disentanglementcited by 13
  32. 2022Adversarial Inter-Group Link Injection Degrades the Fairness of Graph Neural Networkscited by 12
  33. 2023FairGAT: Fairness-aware Graph Attention Networkscited by 12
  34. 2023FairGen: Towards Fair Graph Generationcited by 12
  35. 2023Chasing Fairness in Graphs: A GNN Architecture Perspectivecited by 12
  36. 2021Residual2Vec: Debiasing graph embedding with random graphscited by 11
  37. 2023BeMap: Balanced Message Passing for Fair Graph Neural Networkcited by 11
  38. 2022Graph Learning with Localized Neighborhood Fairnesscited by 10

50 most cited of 126 All 126 (JSON)