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Computer Science > Machine Learning

Title: FairGT: A Fairness-aware Graph Transformer

Abstract: The design of Graph Transformers (GTs) generally neglects considerations for fairness, resulting in biased outcomes against certain sensitive subgroups. Since GTs encode graph information without relying on message-passing mechanisms, conventional fairness-aware graph learning methods cannot be directly applicable to address these issues. To tackle this challenge, we propose FairGT, a Fairness-aware Graph Transformer explicitly crafted to mitigate fairness concerns inherent in GTs. FairGT incorporates a meticulous structural feature selection strategy and a multi-hop node feature integration method, ensuring independence of sensitive features and bolstering fairness considerations. These fairness-aware graph information encodings seamlessly integrate into the Transformer framework for downstream tasks. We also prove that the proposed fair structural topology encoding with adjacency matrix eigenvector selection and multi-hop integration are theoretically effective. Empirical evaluations conducted across five real-world datasets demonstrate FairGT's superiority in fairness metrics over existing graph transformers, graph neural networks, and state-of-the-art fairness-aware graph learning approaches.
Subjects: Machine Learning (cs.LG); Computers and Society (cs.CY)
Journal reference: IJCAI2024
Cite as: arXiv:2404.17169 [cs.LG]
  (or arXiv:2404.17169v1 [cs.LG] for this version)

Submission history

From: Shuo Yu [view email]
[v1] Fri, 26 Apr 2024 05:48:59 GMT (780kb,D)

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