BeMap: Balanced Message Passing for Fair Graph Neural Network

Xiao Lin, Jian Kang, Weilin Cong, Hanghang Tong

Keywords: group fairness, graph neural network, message passing

TL;DR: We propose an easy-to-implement node sampling strategy to generate balanced neighborhoods for learning fair graph neural network.

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.

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Submission Number: 154