mlsanityData to 7 Oct 2026

Fairness Amidst Non-IID Graph Data: A Literature Review

arXiv 2202.07170 ↗First posted 15 Feb 2022cs.LG cs.AIcited by 20

Not on the map. Why?

Abstract (arXiv)

The growing importance of understanding and addressing algorithmic bias in artificial intelligence (AI) has led to a surge in research on AI fairness, which often assumes that the underlying data is independent and identically distributed (IID). However, real-world data frequently exists in non-IID graph structures that capture connections among individual units. To effectively mitigate bias in AI systems, it is essential to bridge the gap between traditional fairness literature, designed for IID data, and the prevalence of non-IID graph data. This survey reviews recent advancements in fairness amidst non-IID graph data, including the newly introduced fair graph generation and the commonly studied fair graph classification. In addition, available datasets and evaluation metrics for future research are identified, the limitations of existing work are highlighted, and promising future directions are proposed.

Key facts. Cited by 20 papers in the map (data through 7 Oct 2026). Builds on 35 of its 112 references that are in the map. First posted on arXiv 15 Feb 2022.

Builds on

35 of 112 references.

References · 35

  1. 2019A Comprehensive Survey on Graph Neural Networkscited by 2,906
  2. 2016node2vec: Scalable Feature Learning for Networkscited by 1,913
  3. 2019A Survey on Bias and Fairness in Machine Learningcited by 1,911
  4. 2019Flexibly Fair Representation Learning by Disentanglementcited by 196
  5. 2017Avoiding Discrimination through Causal Reasoningcited by 191
  6. 2018Fair Clustering Through Fairletscited by 176
  7. 2020Say No to the Discrimination: Learning Fair Graph Neural Networks with Limited Sensitive Attribute Informationcited by 168
  8. 2021A survey on datasets for fairness-aware machine learningcited by 157
  9. 2017Fair Inference On Outcomescited by 143
  10. 2021Towards a Unified Framework for Fair and Stable Graph Representation Learningcited by 128
  11. 2018An Intersectional Definition of Fairnesscited by 126
  12. 2019Compositional Fairness Constraints for Graph Embeddingscited by 126
23 more
  1. 2019Guarantees for Spectral Clustering with Fairness Constraintscited by 72
  2. 2019PC-Fairness: A Unified Framework for Measuring Causality-based Fairnesscited by 67
  3. 2019FAHT: An Adaptive Fairness-aware Decision Tree Classifiercited by 63
  4. 2019DEMO-Net: Degree-specific Graph Neural Networks for Node and Graph Classificationcited by 59
  5. 2022Learning Fair Node Representations with Graph Counterfactual Fairnesscited by 58
  6. 2020DeBayes: a Bayesian Method for Debiasing Network Embeddingscited by 55
  7. 2021Learning Fair Representations for Recommendation: A Graph-based Perspectivecited by 50
  8. 2021FARF: A Fair and Adaptive Random Forests Classifiercited by 37
  9. 2020All of the Fairness for Edge Prediction with Optimal Transportcited by 36
  10. 2021CrossWalk: Fairness-enhanced Node Representation Learningcited by 34
  11. 2021Fair Representation Learning for Heterogeneous Information Networkscited by 28
  12. 2020Disentangled Dynamic Graph Deep Generationcited by 27
  13. 2018A Deterministic Self-Organizing Map Approach and its Application on Satellite Data based Cloud Type Classificationcited by 22
  14. 2019Group-Fairness in Influence Maximizationcited by 21
  15. 2015Influence maximization in complex networks through optimal percolationcited by 20
  16. 2020Exploring Algorithmic Fairness in Robust Graph Covering Problemscited by 19
  17. 2021The KL-Divergence between a Graph Model and its Fair I-Projection as a Fairness Regularizercited by 17
  18. 2020Using Machine Learning to Automate Mammogram Images Analysiscited by 15
  19. 2020Online and Customizable Fairness-aware Learningcited by 14
  20. 2021LSTM Based Sentiment Analysis for Cryptocurrency Predictioncited by 14
  21. 2021Cognitive Visual Commonsense Reasoning Using Dynamic Working Memorycited by 14
  22. 2020Fair Influence Maximization: A Welfare Optimization Approachcited by 12
  23. 2019Gaps in Information Access in Social Networkscited by 9

Built on by

Cited by year

Citing papers per year: 2022: 6; 2023: 5; 2024: 2; 2025: 7.

20 papers.

Sort:

By area:

  1. 2022Bias Mitigation for Machine Learning Classifiers: A Comprehensive Surveycited by 125
  2. 2022Fairness in Graph Mining: A Surveycited by 76
  3. 2023Fairness-Aware Graph Neural Networks: A Surveycited by 22
  4. 2022Longitudinal Fairness with Censorshipcited by 21
  5. 2022Interpreting Unfairness in Graph Neural Networks via Training Node Attributioncited by 18
  6. 2023Fair Decision-making Under Uncertaintycited by 15
  7. 2023On Performance Discrepancies Across Local Homophily Levels in Graph Neural Networkscited by 13
  8. 2025Digital Forensics in the Age of Large Language Modelscited by 10
  9. 2023Deceptive Fairness Attacks on Graphs via Meta Learningcited by 6
  10. 2025Generative AI in Depth: A Survey of Recent Advances, Model Variants, and Real-World Applicationscited by 5
  11. 2024AI-Driven Healthcare: A Review on Ensuring Fairness and Mitigating Biascited by 3
  12. 2025AMCR: A Framework for Assessing and Mitigating Copyright Risks in Generative Modelscited by 3
8 more
  1. 2025Towards Transparent AI: A Survey on Explainable Language Modelscited by 3
  2. 2022Equity and Fairness of Bayesian Knowledge Tracingcited by 2
  3. 2023Marginal Nodes Matter: Towards Structure Fairness in Graphscited by 2
  4. 2024Uncertain Boundaries: Multidisciplinary Approaches to Copyright Issues in Generative AIcited by 1
  5. 2025Fairness-Aware Graph Representation Learning with Limited Demographic Informationcited by 1
  6. 2022Attention Mechanism based Cognition-level Scene Understandingcited by 0
  7. 2025Testing Individual Fairness in Graph Neural Networkscited by 0
  8. 2025Estimating Fair Graphs from Graph-Stationary Datacited by 0