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}}.

翻译：图神经网络（GNNs）的性能提升在数据增强、模型架构设计和训练策略等方面取得了显著进展。针对图公平性问题，现有研究主要通过图数据预处理（如节点特征掩蔽、拓扑重连）或公平训练策略（如正则化、对抗去偏、公平对比学习）来实现公平表征与预测。从模型架构角度探索图公平性的研究尚不充分。尤为重要的是，由于GNN的模型架构（即邻居聚合）会放大偏差，其公平性表现劣于多层感知机。为此，我们旨在通过新型GNN架构实现公平性。本文提出在统一优化框架下设计的公平消息传递机制（FMP），该机制在节点分类任务中，无需数据预处理即可在正向传播过程中显式利用敏感属性，并通过交叉熵损失实现优化。在FMP中，首先采用聚合操作利用邻居信息，随后通过偏差缓解步骤将人口统计群体节点表征中心显式对齐。通过这种方式，FMP方案既能聚合邻居有效信息，又能缓解偏差，从而实现公平性与预测性能的更优权衡。节点分类任务实验表明，所提出的FMP在三个真实数据集上的公平性和准确率均优于多个基线方法。代码已开源在{\url{https://github.com/zhimengj0326/FMP}}。