Chasing Fairness in Graphs: A GNN Architecture Perspective
Zhimeng Jiang Xiaotian Han Chao Fan Zirui Liu Na Zou Ali Mostafavi Xia Hu
There has been significant progress in improving the performance of graph
neural networks (GNNs) through enhancements in graph data, model architecture
design, and training strategies. For fairness in graphs, recent studies achieve
fair representations and predictions through either graph data pre-processing
(e.g., node feature masking, and topology rewiring) or fair training strategies
(e.g., regularization, adversarial debiasing, and fair contrastive learning).
How to achieve fairness in graphs from the model architecture perspective is
less explored. More importantly, GNNs exhibit worse fairness performance
compared to multilayer perception since their model architecture (i.e.,
neighbor aggregation) amplifies biases. To this end, we aim to achieve fairness
via a new GNN architecture. We propose \textsf{F}air \textsf{M}essage
\textsf{P}assing (FMP) designed within a unified optimization framework for
GNNs. Notably, FMP \textit{explicitly} renders sensitive attribute usage in
\textit{forward propagation} for node classification task using cross-entropy
loss without data pre-processing. In FMP, the aggregation is first adopted to
utilize neighbors' information and then the bias mitigation step explicitly
pushes demographic group node presentation centers together. In this way, FMP
scheme can aggregate useful information from neighbors and mitigate bias to
achieve better fairness and prediction tradeoff performance. Experiments on
node classification tasks demonstrate that the proposed FMP outperforms several
baselines in terms of fairness and accuracy on three real-world datasets. The
code is available in {\url{https://github.com/zhimengj0326/FMP}}.