Group fairness for Graph Neural Networks (GNNs), which emphasizes algorithmic decisions neither favoring nor harming certain groups defined by sensitive attributes (e.g., race and gender), has gained considerable attention. In particular, the objective of group fairness is to ensure that the decisions made by GNNs are independent of the sensitive attribute. To achieve this objective, most existing approaches involve eliminating sensitive attribute information in node representations or algorithmic decisions. However, such ways may also eliminate task-related information due to its inherent correlation with the sensitive attribute, leading to a sacrifice in utility. In this work, we focus on improving the fairness of GNNs while preserving task-related information and propose a fair GNN framework named FairSAD. Instead of eliminating sensitive attribute information, FairSAD enhances the fairness of GNNs via Sensitive Attribute Disentanglement (SAD), which separates the sensitive attribute-related information into an independent component to mitigate its impact. Additionally, FairSAD utilizes a channel masking mechanism to adaptively identify the sensitive attribute-related component and subsequently decorrelates it. Overall, FairSAD minimizes the impact of the sensitive attribute on GNN outcomes rather than eliminating sensitive attributes, thereby preserving task-related information associated with the sensitive attribute. Furthermore, experiments conducted on several real-world datasets demonstrate that FairSAD outperforms other state-of-the-art methods by a significant margin in terms of both fairness and utility performance. Our source code is available at https://github.com/ZzoomD/FairSAD.

翻译：图神经网络（GNNs）的群体公平性要求算法决策既不偏向也不损害由敏感属性（如种族和性别）定义的特定群体，这一问题已受到广泛关注。群体公平性的目标是确保GNNs的决策独立于敏感属性。为实现该目标，现有方法通常消除节点表示或算法决策中的敏感属性信息。然而，由于敏感属性与任务相关信息存在固有相关性，此类做法可能同时消除任务相关信息，导致效用损失。本文致力于在保留任务相关信息的同时提升GNNs的公平性，提出名为FairSAD的公平图神经网络框架。与消除敏感属性信息不同，FairSAD通过敏感属性解耦（SAD）将敏感属性相关信息分离为独立分量以减轻其影响，从而增强GNNs的公平性。此外，FairSAD采用通道掩蔽机制自适应识别敏感属性相关分量，并对其进行去相关处理。总体而言，FairSAD通过最小化敏感属性对GNN输出结果的影响（而非彻底消除敏感属性），在保留与敏感属性关联的任务相关信息的同时实现公平性目标。在多个真实数据集上的实验表明，FairSAD在公平性和效用性能方面均显著优于现有最先进方法。我们的源代码已开源至https://github.com/ZzoomD/FairSAD。