Towards Fair Graph Neural Networks via Graph Counterfactual

Zhimeng Guo, Jialiang Li, Teng Xiao, Yao Ma, Suhang Wang

Research output: Chapter in Book/Report/Conference proceedingConference contribution

2 Scopus citations


Graph neural networks have shown great ability in representation (GNNs) learning on graphs, facilitating various tasks. Despite their great performance in modeling graphs, recent works show that GNNs tend to inherit and amplify the bias from training data, causing concerns of the adoption of GNNs in high-stake scenarios. Hence, many efforts have been taken for fairness-aware GNNs. However, most existing fair GNNs learn fair node representations by adopting statistical fairness notions, which may fail to alleviate bias in the presence of statistical anomalies. Motivated by causal theory, there are several attempts utilizing graph counterfactual fairness to mitigate root causes of unfairness. However, these methods suffer from non-realistic counterfactuals obtained by perturbation or generation. In this paper, we take a causal view on fair graph learning problem. Guided by the casual analysis, we propose a novel framework CAF, which can select counterfactuals from training data to avoid non-realistic counterfactuals and adopt selected counterfactuals to learn fair node representations for node classification task. Extensive experiments on synthetic and real-world datasets show the effectiveness of CAF. Our code is available at

Original languageEnglish (US)
Title of host publicationCIKM 2023 - Proceedings of the 32nd ACM International Conference on Information and Knowledge Management
PublisherAssociation for Computing Machinery
Number of pages10
ISBN (Electronic)9798400701245
StatePublished - Oct 21 2023
Externally publishedYes
Event32nd ACM International Conference on Information and Knowledge Management, CIKM 2023 - Birmingham, United Kingdom
Duration: Oct 21 2023Oct 25 2023

Publication series

NameInternational Conference on Information and Knowledge Management, Proceedings


Conference32nd ACM International Conference on Information and Knowledge Management, CIKM 2023
Country/TerritoryUnited Kingdom

All Science Journal Classification (ASJC) codes

  • General Business, Management and Accounting
  • General Decision Sciences


  • Causal learning
  • Counterfactual fairness
  • Graph neural networks


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