Fair Graph Representation Learning via Sensitive Attribute Disentanglement
<|reference_start|>Fair Graph Representation Learning via Sensitive Attribute Disentanglement: 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.<|reference_end|>
@article{zhu2024fair,
    title={Fair Graph Representation Learning via Sensitive Attribute
  Disentanglement},
    author={Yuchang Zhu, Jintang Li, Zibin Zheng, Liang Chen},
    journal={arXiv preprint arXiv:2405.07011},
    year={2024},
    archivePrefix={arXiv},
    eprint={2405.07011},
    primaryClass={cs.LG cs.CY}
}