mlsanityData to 7 Oct 2026

Fairness in Graph Mining: A Survey

arXiv 2204.09888 ↗First posted 21 Apr 2022cs.LG cs.CYcited by 76

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Abstract (arXiv)

Graph mining algorithms have been playing a significant role in myriad fields over the years. However, despite their promising performance on various graph analytical tasks, most of these algorithms lack fairness considerations. As a consequence, they could lead to discrimination towards certain populations when exploited in human-centered applications. Recently, algorithmic fairness has been extensively studied in graph-based applications. In contrast to algorithmic fairness on independent and identically distributed (i.i.d.) data, fairness in graph mining has exclusive backgrounds, taxonomies, and fulfilling techniques. In this survey, we provide a comprehensive and up-to-date introduction of existing literature under the context of fair graph mining. Specifically, we propose a novel taxonomy of fairness notions on graphs, which sheds light on their connections and differences. We further present an organized summary of existing techniques that promote fairness in graph mining. Finally, we summarize the widely used datasets in this emerging research field and provide insights on current research challenges and open questions, aiming at encouraging cross-breeding ideas and further advances.

Key facts. Cited by 76 papers in the map (data through 7 Oct 2026). Builds on 87 of its 197 references that are in the map. First posted on arXiv 21 Apr 2022.

Builds on

87 of 197 references.

References · 87

  1. 2016Semi-Supervised Classification with Graph Convolutional Networkscited by 11,282
  2. 2017Graph Attention Networkscited by 8,854
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  10. 2016Semantics derived automatically from language corpora contain human-like biasescited by 894
  11. 2020Simple and Deep Graph Convolutional Networkscited by 857
  12. 2017Counterfactual Fairnesscited by 802
38 more
  1. 2016Ups and Downs: Modeling the Visual Evolution of Fashion Trends with One-Class Collaborative Filteringcited by 517
  2. 2020Fairness in Machine Learning: A Surveycited by 369
  3. 2021Graph Neural Network for Traffic Forecasting: A Surveycited by 260
  4. 2017Word Embeddings Quantify 100 Years of Gender and Ethnic Stereotypescited by 251
  5. 2020Bias and Debias in Recommender System: A Survey and Future Directionscited by 239
  6. 2015Censoring Representations with an Adversarycited by 191
  7. 2018Fair Clustering Through Fairletscited by 176
  8. 2020Say No to the Discrimination: Learning Fair Graph Neural Networks with Limited Sensitive Attribute Informationcited by 168
  9. 2020A Semi-supervised Graph Attentive Network for Financial Fraud Detectioncited by 150
  10. 2017Fairness in Machine Learning: Lessons from Political Philosophycited by 148
  11. 2019Identifying and Correcting Label Bias in Machine Learningcited by 146
  12. 2020Algorithmic Fairnesscited by 143
  13. 2021Towards a Unified Framework for Fair and Stable Graph Representation Learningcited by 128
  14. 2019Compositional Fairness Constraints for Graph Embeddingscited by 126
  15. 2019On the Apparent Conflict Between Individual and Group Fairnesscited by 126
  16. 2019Fairness in Deep Learning: A Computational Perspectivecited by 123
  17. 2013Discrimination in Online Ad Deliverycited by 93
  18. 2017Beyond Parity: Fairness Objectives for Collaborative Filteringcited by 93
  19. 2021EDITS: Modeling and Mitigating Data Bias for Graph Neural Networkscited by 92
  20. 2020Molecular graph generation with Graph Neural Networkscited by 79
  21. 2020FairRec: Two-Sided Fairness for Personalized Recommendations in Two-Sided Platformscited by 77
  22. 2021Towards Long-term Fairness in Recommendationcited by 77
  23. 2020Fairness by Learning Orthogonal Disentangled Representationscited by 76
  24. 2021User-oriented Fairness in Recommendationcited by 75
  25. 2020Fairness-Aware Explainable Recommendation over Knowledge Graphscited by 74
  26. 2019Guarantees for Spectral Clustering with Fairness Constraintscited by 72
  27. 2021Subgroup Generalization and Fairness of Graph Neural Networkscited by 69
  28. 2019The Unfairness of Popularity Bias in Recommendationcited by 68
  29. 2019Operationalizing Individual Fairness with Pairwise Fair Representationscited by 64
  30. 2021Fairness in Rankings and Recommendations: An Overviewcited by 62
  31. 2020Poisoning Attacks on Algorithmic Fairnesscited by 58
  32. 2022Learning Fair Node Representations with Graph Counterfactual Fairnesscited by 58
  33. 2018Fairness Through Computationally-Bounded Awarenesscited by 55
  34. 2020DeBayes: a Bayesian Method for Debiasing Network Embeddingscited by 55
  35. 2021Learning Fair Representations for Recommendation: A Graph-based Perspectivecited by 50
  36. 2020Exacerbating Algorithmic Bias through Fairness Attackscited by 47
  37. 2020FairRec: Fairness-aware News Recommendation with Decomposed Adversarial Learningcited by 44
  38. 2020All of the Fairness for Edge Prediction with Optimal Transportcited by 36

50 most cited of 87 All 87 (JSON)

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Cited by year (2026 to 7 Oct)

Citing papers per year: 2022: 6; 2023: 21; 2024: 26; 2025: 16; 2026: 7.

76 papers.

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  1. 2023Better to Ask in English: Cross-Lingual Evaluation of Large Language Models for Healthcare Queriescited by 82
  2. 2023A Comprehensive Survey on Deep Graph Representation Learningcited by 72
  3. 2024Fairness in Large Language Models: A Taxonomic Surveycited by 57
  4. 2024FairGT: A Fairness-aware Graph Transformercited by 29
  5. 2022Fairness and Explainability: Bridging the Gap Towards Fair Model Explanationscited by 22
  6. 2023Counterfactual Learning on Graphs: A Surveycited by 22
  7. 2023Fairness-Aware Graph Neural Networks: A Surveycited by 22
  8. 2022A Survey on Fairness for Machine Learning on Graphscited by 20
  9. 2022Interpreting Unfairness in Graph Neural Networks via Training Node Attributioncited by 18
  10. 2024CEB: Compositional Evaluation Benchmark for Fairness in Large Language Modelscited by 17
  11. 2023The Devil is in the Data: Learning Fair Graph Neural Networks via Partial Knowledge Distillationcited by 16
  12. 2024A Survey of Deep Graph Learning under Distribution Shifts: from Graph Out-of-Distribution Generalization to Adaptationcited by 16
38 more
  1. 2022On Structural Explanation of Bias in Graph Neural Networkscited by 15
  2. 2023A Survey of Imbalanced Learning on Graphs: Problems, Techniques, and Future Directionscited by 15
  3. 2023On Performance Discrepancies Across Local Homophily Levels in Graph Neural Networkscited by 13
  4. 2025Graph2text or Graph2token: A Perspective of Large Language Models for Graph Learningcited by 12
  5. 2024FUGNN: Harmonizing Fairness and Utility in Graph Neural Networkscited by 11
  6. 2024Rethinking Fair Graph Neural Networks from Re-balancingcited by 11
  7. 2023RELIANT: Fair Knowledge Distillation for Graph Neural Networkscited by 10
  8. 2025A Generic Framework for Conformal Fairnesscited by 7
  9. 2023GFairHint: Improving Individual Fairness for Graph Neural Networks via Fairness Hintcited by 6
  10. 2023Deceptive Fairness Attacks on Graphs via Meta Learningcited by 6
  11. 2023Data Optimization in Deep Learning: A Surveycited by 6
  12. 2024FairWire: Fair Graph Generationcited by 6
  13. 2024Towards Fair Graph Anomaly Detection: Problem, Benchmark Datasets, and Evaluationcited by 6
  14. 2024Promoting Fairness in Link Prediction with Graph Enhancementcited by 6
  15. 2024Unveiling the Impact of Local Homophily on GNN Fairness: In-Depth Analysis and New Benchmarkscited by 6
  16. 2025DHG-Bench: A Comprehensive Benchmark for Deep Hypergraph Learningcited by 5
  17. 2022FairMILE: Towards an Efficient Framework for Fair Graph Representation Learningcited by 4
  18. 2023Adversarial Attacks on Fairness of Graph Neural Networkscited by 4
  19. 2023When Graph Neural Network Meets Causality: Opportunities, Methodologies and An Outlookcited by 4
  20. 2024Towards Cohesion-Fairness Harmony: Contrastive Regularization in Individual Fair Graph Clusteringcited by 4
  21. 2024Causal Inference with Latent Variables: Recent Advances and Future Prospectivescited by 4
  22. 2026FairGE: Fairness-Aware Graph Encoding in Incomplete Social Networkscited by 4
  23. 2023Spectral Normalized-Cut Graph Partitioning with Fairness Constraintscited by 3
  24. 2024Unveiling the Deficiencies of Pre-trained Text-and-Layout Models in Real-world Visually-rich Document Information Extractioncited by 3
  25. 2024On Explaining Unfairness: An Overviewcited by 3
  26. 2024On the Topology Awareness and Generalization Performance of Graph Neural Networkscited by 3
  27. 2024Enhancing Fairness in Unsupervised Graph Anomaly Detection through Disentanglementcited by 3
  28. 2024A Survey of Out-of-distribution Generalization for Graph Machine Learning from a Causal Viewcited by 3
  29. 2022Balancing Utility and Fairness in Submodular Maximization (Technical Report)cited by 2
  30. 2023Fair Few-shot Learning with Auxiliary Setscited by 2
  31. 2023Marginal Nodes Matter: Towards Structure Fairness in Graphscited by 2
  32. 2024MAPPING: Debiasing Graph Neural Networks for Fair Node Classification with Limited Sensitive Information Leakagecited by 2
  33. 2024GRAPHGINI: Fostering Individual and Group Fairness in Graph Neural Networkscited by 2
  34. 2024Block-Diagonal Guided DBSCAN Clusteringcited by 2
  35. 2024Algorithmic Fairness: A Tolerance Perspectivecited by 2
  36. 2024A Benchmark for Fairness-Aware Graph Learningcited by 2
  37. 2025Fairness in Augmented Graph Learning: A Surveycited by 2
  38. 2025Benchmarking Fairness-aware Graph Neural Networks in Knowledge Graphscited by 2

50 most cited of 76 All 76 (JSON)