# Fairness Amidst Non-IID Graph Data: A Literature Review

arXiv: https://arxiv.org/abs/2202.07170 · HTML: https://mlsanity.com/paper/2202.07170/

## Key facts

- Cited by 20 papers in the map (data through 14 Oct 2026).
- Builds on 35 of its 112 references that are in the map.
- First posted on arXiv 15 Feb 2022.

## Builds on (most cited first, 30 of 35)

- [A Comprehensive Survey on Graph Neural Networks](https://mlsanity.com/paper/1901.00596/) (2019)
- [node2vec: Scalable Feature Learning for Networks](https://mlsanity.com/paper/1607.00653/) (2016)
- [A Survey on Bias and Fairness in Machine Learning](https://mlsanity.com/paper/1908.09635/) (2019)
- [Flexibly Fair Representation Learning by Disentanglement](https://mlsanity.com/paper/1906.02589/) (2019)
- [Avoiding Discrimination through Causal Reasoning](https://mlsanity.com/paper/1706.02744/) (2017)
- [Fair Clustering Through Fairlets](https://mlsanity.com/paper/1802.05733/) (2018)
- [Say No to the Discrimination: Learning Fair Graph Neural Networks with Limited Sensitive Attribute Information](https://mlsanity.com/paper/2009.01454/) (2020)
- [A survey on datasets for fairness-aware machine learning](https://mlsanity.com/paper/2110.00530/) (2021)
- [Fair Inference On Outcomes](https://mlsanity.com/paper/1705.10378/) (2017)
- [Towards a Unified Framework for Fair and Stable Graph Representation Learning](https://mlsanity.com/paper/2102.13186/) (2021)
- [An Intersectional Definition of Fairness](https://mlsanity.com/paper/1807.08362/) (2018)
- [Compositional Fairness Constraints for Graph Embeddings](https://mlsanity.com/paper/1905.10674/) (2019)
- [Guarantees for Spectral Clustering with Fairness Constraints](https://mlsanity.com/paper/1901.08668/) (2019)
- [PC-Fairness: A Unified Framework for Measuring Causality-based Fairness](https://mlsanity.com/paper/1910.12586/) (2019)
- [FAHT: An Adaptive Fairness-aware Decision Tree Classifier](https://mlsanity.com/paper/1907.07237/) (2019)
- [DEMO-Net: Degree-specific Graph Neural Networks for Node and Graph Classification](https://mlsanity.com/paper/1906.02319/) (2019)
- [Learning Fair Node Representations with Graph Counterfactual Fairness](https://mlsanity.com/paper/2201.03662/) (2022)
- [DeBayes: a Bayesian Method for Debiasing Network Embeddings](https://mlsanity.com/paper/2002.11442/) (2020)
- [Learning Fair Representations for Recommendation: A Graph-based Perspective](https://mlsanity.com/paper/2102.09140/) (2021)
- [FARF: A Fair and Adaptive Random Forests Classifier](https://mlsanity.com/paper/2108.07403/) (2021)
- [All of the Fairness for Edge Prediction with Optimal Transport](https://mlsanity.com/paper/2010.16326/) (2020)
- [CrossWalk: Fairness-enhanced Node Representation Learning](https://mlsanity.com/paper/2105.02725/) (2021)
- [Fair Representation Learning for Heterogeneous Information Networks](https://mlsanity.com/paper/2104.08769/) (2021)
- [Disentangled Dynamic Graph Deep Generation](https://mlsanity.com/paper/2010.07276/) (2020)
- [A Deterministic Self-Organizing Map Approach and its Application on Satellite Data based Cloud Type Classification](https://mlsanity.com/paper/1808.08315/) (2018)
- [Influence maximization in complex networks through optimal percolation](https://mlsanity.com/paper/1506.08326/) (2015)
- [Group-Fairness in Influence Maximization](https://mlsanity.com/paper/1903.00967/) (2019)
- [Exploring Algorithmic Fairness in Robust Graph Covering Problems](https://mlsanity.com/paper/2006.06865/) (2020)
- [The KL-Divergence between a Graph Model and its Fair I-Projection as a Fairness Regularizer](https://mlsanity.com/paper/2103.01846/) (2021)
- [Using Machine Learning to Automate Mammogram Images Analysis](https://mlsanity.com/paper/2012.03151/) (2020)

## Built on by (most cited first, 20 of 20)

- [Bias Mitigation for Machine Learning Classifiers: A Comprehensive Survey](https://mlsanity.com/paper/2207.07068/) (2022)
- [Fairness in Graph Mining: A Survey](https://mlsanity.com/paper/2204.09888/) (2022)
- [Longitudinal Fairness with Censorship](https://mlsanity.com/paper/2203.16024/) (2022)
- [Fairness-Aware Graph Neural Networks: A Survey](https://mlsanity.com/paper/2307.03929/) (2023)
- [Interpreting Unfairness in Graph Neural Networks via Training Node Attribution](https://mlsanity.com/paper/2211.14383/) (2022)
- [Fair Decision-making Under Uncertainty](https://mlsanity.com/paper/2301.12364/) (2023)
- [On Performance Discrepancies Across Local Homophily Levels in Graph Neural Networks](https://mlsanity.com/paper/2306.05557/) (2023)
- [Digital Forensics in the Age of Large Language Models](https://mlsanity.com/paper/2504.02963/) (2025)
- [Deceptive Fairness Attacks on Graphs via Meta Learning](https://mlsanity.com/paper/2310.15653/) (2023)
- [Generative AI in Depth: A Survey of Recent Advances, Model Variants, and Real-World Applications](https://mlsanity.com/paper/2510.21887/) (2025)
- [AI-Driven Healthcare: A Review on Ensuring Fairness and Mitigating Bias](https://mlsanity.com/paper/2407.19655/) (2024)
- [AMCR: A Framework for Assessing and Mitigating Copyright Risks in Generative Models](https://mlsanity.com/paper/2509.00641/) (2025)
- [Towards Transparent AI: A Survey on Explainable Language Models](https://mlsanity.com/paper/2509.21631/) (2025)
- [Equity and Fairness of Bayesian Knowledge Tracing](https://mlsanity.com/paper/2205.02333/) (2022)
- [Marginal Nodes Matter: Towards Structure Fairness in Graphs](https://mlsanity.com/paper/2310.14527/) (2023)
- [Uncertain Boundaries: Multidisciplinary Approaches to Copyright Issues in Generative AI](https://mlsanity.com/paper/2404.08221/) (2024)
- [Fairness-Aware Graph Representation Learning with Limited Demographic Information](https://mlsanity.com/paper/2511.13540/) (2025)
- [Attention Mechanism based Cognition-level Scene Understanding](https://mlsanity.com/paper/2204.08027/) (2022)
- [Testing Individual Fairness in Graph Neural Networks](https://mlsanity.com/paper/2504.18353/) (2025)
- [Estimating Fair Graphs from Graph-Stationary Data](https://mlsanity.com/paper/2510.07536/) (2025)

## 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.