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Learning Fair Node Representations with Graph Counterfactual Fairness

Authors:

Jundong LiAuthors Info & Claims

WSDM '22: Proceedings of the Fifteenth ACM International Conference on Web Search and Data Mining

Pages 695 - 703

https://doi.org/10.1145/3488560.3498391

Published: 15 February 2022 Publication History

Abstract

Fair machine learning aims to mitigate the biases of model predictions against certain subpopulations regarding sensitive attributes such as race and gender. Among the many existing fairness notions, counterfactual fairness measures the model fairness from a causal perspective by comparing the predictions of each individual from the original data and the counterfactuals. In counterfactuals, the sensitive attribute values of this individual had been modified. Recently, a few works extend counterfactual fairness to graph data, but most of them neglect the following facts that can lead to biases: 1) the sensitive attributes of each node's neighbors may causally affect the prediction w.r.t. this node; 2) the sensitive attributes may causally affect other features and the graph structure. To tackle these issues, in this paper, we propose a novel fairness notion - graph counterfactual fairness, which considers the biases led by the above facts. To learn node representations towards graph counterfactual fairness, we propose a novel framework based on counterfactual data augmentation. In this framework, we generate counterfactuals corresponding to perturbations on each node's and their neighbors' sensitive attributes. Then we enforce fairness by minimizing the discrepancy between the representations learned from the original graph and the counterfactuals for each node. Experiments on both synthetic and real-world graphs show that our framework outperforms the state-of-the-art baselines in graph counterfactual fairness, and also achieves comparable prediction performance.

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Cited By

Zhang GYuan GCheng DLiu LLi JZhang S(2025)Disentangled contrastive learning for fair graph representationsNeural Networks10.1016/j.neunet.2024.106781181(106781)Online publication date: Jan-2025
https://doi.org/10.1016/j.neunet.2024.106781
Guo ZWu ZXiao TAggarwal CLiu HWang S(2025)Counterfactual Learning on Graphs: A SurveyMachine Intelligence Research10.1007/s11633-024-1519-z22:1(17-59)Online publication date: 24-Jan-2025
https://doi.org/10.1007/s11633-024-1519-z
Zhang WZhou SWalsh TWeiss J(2025)Fairness amidst non‐IID graph data: A literature reviewAI Magazine10.1002/aaai.1221246:1Online publication date: 28-Jan-2025
https://doi.org/10.1002/aaai.12212
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Index Terms

Learning Fair Node Representations with Graph Counterfactual Fairness
1. Applied computing
  1. Law, social and behavioral sciences
2. Computing methodologies
  1. Machine learning

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Information & Contributors

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Published In

cover image ACM Conferences

WSDM '22: Proceedings of the Fifteenth ACM International Conference on Web Search and Data Mining

February 2022

1690 pages

ISBN:9781450391320

DOI:10.1145/3488560

General Chairs:
K. Selcuk Candan
Arizona State University, USA
,
Huan Liu
Arizona State University, USA
,
Program Chairs:
Leman Akoglu
Carnegie Mellon University, USA
,
Xin Luna Dong
Meta Platforms, Inc. (former Facebook), USA
,
Jiliang Tang
Michigan State University, USA

Copyright © 2022 ACM.

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Published: 15 February 2022

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JP Morgan Chase Faculty Research Award
Cisco Faculty Research Award
National Science Foundation (NSF)

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WSDM '22

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WSDM '22: The Fifteenth ACM International Conference on Web Search and Data Mining

February 21 - 25, 2022

AZ, Virtual Event, USA

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Cited By

Zhang GYuan GCheng DLiu LLi JZhang S(2025)Disentangled contrastive learning for fair graph representationsNeural Networks10.1016/j.neunet.2024.106781181(106781)Online publication date: Jan-2025
https://doi.org/10.1016/j.neunet.2024.106781
Guo ZWu ZXiao TAggarwal CLiu HWang S(2025)Counterfactual Learning on Graphs: A SurveyMachine Intelligence Research10.1007/s11633-024-1519-z22:1(17-59)Online publication date: 24-Jan-2025
https://doi.org/10.1007/s11633-024-1519-z
Zhang WZhou SWalsh TWeiss J(2025)Fairness amidst non‐IID graph data: A literature reviewAI Magazine10.1002/aaai.1221246:1Online publication date: 28-Jan-2025
https://doi.org/10.1002/aaai.12212
Jin JLi HFeng FSalakhutdinov RKolter ZHeller KWeller AOliver NScarlett JBerkenkamp F(2024)On the maximal local disparity of fairness-aware classifiersProceedings of the 41st International Conference on Machine Learning10.5555/3692070.3692959(22115-22144)Online publication date: 21-Jul-2024
https://dl.acm.org/doi/10.5555/3692070.3692959
Liu YChen HImani M(2024)Promoting fairness in link prediction with graph enhancementFrontiers in Big Data10.3389/fdata.2024.14893067Online publication date: 24-Oct-2024
https://doi.org/10.3389/fdata.2024.1489306
Medda GFabbri FMarras MBoratto LFenu G(2024)GNNUERS: Fairness Explanation in GNNs for Recommendation via Counterfactual ReasoningACM Transactions on Intelligent Systems and Technology10.1145/365563116:1(1-26)Online publication date: 26-Dec-2024
https://dl.acm.org/doi/10.1145/3655631
Choe MYoo JLee GBaek WKang UShin K(2024)Representative and Back-In-Time Sampling from Real-world HypergraphsACM Transactions on Knowledge Discovery from Data10.1145/365330618:6(1-48)Online publication date: 26-Apr-2024
https://dl.acm.org/doi/10.1145/3653306
Lalor JAbbasi AOketch KYang YForsgren N(2024)Should Fairness be a Metric or a Model? A Model-based Framework for Assessing Bias in Machine Learning PipelinesACM Transactions on Information Systems10.1145/364127642:4(1-41)Online publication date: 22-Mar-2024
https://dl.acm.org/doi/10.1145/3641276
Chen WWu YZhang ZZhuang FHe ZXie RXia F(2024)FairGap: Fairness-Aware Recommendation via Generating Counterfactual GraphACM Transactions on Information Systems10.1145/363835242:4(1-25)Online publication date: 9-Feb-2024
https://dl.acm.org/doi/10.1145/3638352
Yang WWang HLi HZou HJin RKuang KCui PBaeza-Yates RBonchi F(2024)Your Neighbor Matters: Towards Fair Decisions Under Networked InterferenceProceedings of the 30th ACM SIGKDD Conference on Knowledge Discovery and Data Mining10.1145/3637528.3671960(3829-3840)Online publication date: 25-Aug-2024
https://dl.acm.org/doi/10.1145/3637528.3671960
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