BeMap: Balanced Message Passing for Fair Graph Neural Network

• URL: http://arxiv.org/abs/2306.04107v2
• Date: Fri, 8 Mar 2024 22:40:45 GMT
• Title: BeMap: Balanced Message Passing for Fair Graph Neural Network
• Authors: Xiao Lin, Jian Kang, Weilin Cong, Hanghang Tong
• Abstract summary: We show that message passing could amplify the bias when the 1-hop neighbors from different demographic groups are unbalanced. We propose BeMap, a fair message passing method, that balances the number of the 1-hop neighbors of each node among different demographic groups.
• Score: 50.910842893257275
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Fairness in graph neural networks has been actively studied recently. However, existing works often do not explicitly consider the role of message passing in introducing or amplifying the bias. In this paper, we first investigate the problem of bias amplification in message passing. We empirically and theoretically demonstrate that message passing could amplify the bias when the 1-hop neighbors from different demographic groups are unbalanced. Guided by such analyses, we propose BeMap, a fair message passing method, that leverages a balance-aware sampling strategy to balance the number of the 1-hop neighbors of each node among different demographic groups. Extensive experiments on node classification demonstrate the efficacy of BeMap in mitigating bias while maintaining classification accuracy. The code is available at https://github.com/xiaolin-cs/BeMap.

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