# Graph Learning with Localized Neighborhood Fairness

arXiv: https://arxiv.org/abs/2212.12040 · HTML: https://mlsanity.com/paper/2212.12040/

## Key facts

- Cited by 10 papers in the map (data through 14 Oct 2026).
- Builds on 33 of its 55 references that are in the map.
- First posted on arXiv 22 Dec 2022.

## Builds on (most cited first, 30 of 33)

- [Semi-Supervised Classification with Graph Convolutional Networks](https://mlsanity.com/paper/1609.02907/) (2016)
- [Graph Attention Networks](https://mlsanity.com/paper/1710.10903/) (2017)
- [node2vec: Scalable Feature Learning for Networks](https://mlsanity.com/paper/1607.00653/) (2016)
- [Simplifying Graph Convolutional Networks](https://mlsanity.com/paper/1902.07153/) (2019)
- [Representation Learning on Graphs: Methods and Applications](https://mlsanity.com/paper/1709.05584/) (2017)
- [Flexibly Fair Representation Learning by Disentanglement](https://mlsanity.com/paper/1906.02589/) (2019)
- [Censoring Representations with an Adversary](https://mlsanity.com/paper/1511.05897/) (2015)
- [Say No to the Discrimination: Learning Fair Graph Neural Networks with Limited Sensitive Attribute Information](https://mlsanity.com/paper/2009.01454/) (2020)
- [Adaptive Graph Convolutional Neural Networks](https://mlsanity.com/paper/1801.03226/) (2018)
- [Towards a Unified Framework for Fair and Stable Graph Representation Learning](https://mlsanity.com/paper/2102.13186/) (2021)
- [Compositional Fairness Constraints for Graph Embeddings](https://mlsanity.com/paper/1905.10674/) (2019)
- [EDITS: Modeling and Mitigating Data Bias for Graph Neural Networks](https://mlsanity.com/paper/2108.05233/) (2021)
- [Guarantees for Spectral Clustering with Fairness Constraints](https://mlsanity.com/paper/1901.08668/) (2019)
- [FairDrop: Biased Edge Dropout for Enhancing Fairness in Graph Representation Learning](https://mlsanity.com/paper/2104.14210/) (2021)
- [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)
- [Personalized Counterfactual Fairness in Recommendation](https://mlsanity.com/paper/2105.09829/) (2021)
- [All of the Fairness for Edge Prediction with Optimal Transport](https://mlsanity.com/paper/2010.16326/) (2020)
- [FMP: Toward Fair Graph Message Passing against Topology Bias](https://mlsanity.com/paper/2202.04187/) (2022)
- [CrossWalk: Fairness-enhanced Node Representation Learning](https://mlsanity.com/paper/2105.02725/) (2021)
- [Unbiased Graph Embedding with Biased Graph Observations](https://mlsanity.com/paper/2110.13957/) (2021)
- [Fair Node Representation Learning via Adaptive Data Augmentation](https://mlsanity.com/paper/2201.08549/) (2022)
- [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)
- [Adversarial Learning for Debiasing Knowledge Graph Embeddings](https://mlsanity.com/paper/2006.16309/) (2020)
- [Adversarial Graph Embeddings for Fair Influence Maximization over Social Networks](https://mlsanity.com/paper/2005.04074/) (2020)
- [FairEdit: Preserving Fairness in Graph Neural Networks through Greedy Graph Editing](https://mlsanity.com/paper/2201.03681/) (2022)
- [Fair Influence Maximization: A Welfare Optimization Approach](https://mlsanity.com/paper/2006.07906/) (2020)
- [MONET: Debiasing Graph Embeddings via the Metadata-Orthogonal Training Unit](https://mlsanity.com/paper/1909.11793/) (2019)

## Built on by (most cited first, 10 of 10)

- [Fairness-Aware Graph Neural Networks: A Survey](https://mlsanity.com/paper/2307.03929/) (2023)
- [On Performance Discrepancies Across Local Homophily Levels in Graph Neural Networks](https://mlsanity.com/paper/2306.05557/) (2023)
- [BeMap: Balanced Message Passing for Fair Graph Neural Network](https://mlsanity.com/paper/2306.04107/) (2023)
- [Mitigating Degree Bias in Signed Graph Neural Networks](https://mlsanity.com/paper/2408.08508/) (2024)
- [Structural Bias Beyond Homophily: A Study of Fairness in Link Prediction](https://mlsanity.com/paper/2602.11802/) (2026)
- [Trustworthy GNNs with LLMs: A Systematic Review and Taxonomy](https://mlsanity.com/paper/2502.08353/) (2025)
- [Reproducibility Study Of Learning Fair Graph Representations Via Automated Data Augmentations](https://mlsanity.com/paper/2409.00421/) (2024)
- [Estimating Fair Graphs from Graph-Stationary Data](https://mlsanity.com/paper/2510.07536/) (2025)
- [Mitigating topology biases in Graph Diffusion via Counterfactual Intervention](https://mlsanity.com/paper/2603.02005/) (2026)
- [k-hop Fairness: Addressing Disparities in Graph Link Prediction Beyond First-Order Neighborhoods](https://mlsanity.com/paper/2603.03867/) (2026)

## Abstract (arXiv)

Learning fair graph representations for downstream applications is becoming increasingly important, but existing work has mostly focused on improving fairness at the global level by either modifying the graph structure or objective function without taking into account the local neighborhood of a node. In this work, we formally introduce the notion of neighborhood fairness and develop a computational framework for learning such locally fair embeddings. We argue that the notion of neighborhood fairness is more appropriate since GNN-based models operate at the local neighborhood level of a node. Our neighborhood fairness framework has two main components that are flexible for learning fair graph representations from arbitrary data: the first aims to construct fair neighborhoods for any arbitrary node in a graph and the second enables adaption of these fair neighborhoods to better capture certain application or data-dependent constraints, such as allowing neighborhoods to be more biased towards certain attributes or neighbors in the graph.Furthermore, while link prediction has been extensively studied, we are the first to investigate the graph representation learning task of fair link classification. We demonstrate the effectiveness of the proposed neighborhood fairness framework for a variety of graph machine learning tasks including fair link prediction, link classification, and learning fair graph embeddings. Notably, our approach achieves not only better fairness but also increases the accuracy in the majority of cases across a wide variety of graphs, problem settings, and metrics.