Despite remarkable success in diverse web-based applications, Graph Neural
Networks(GNNs) inherit and further exacerbate historical discrimination and
social stereotypes, which critically hinder their deployments in high-stake
domains such as online clinical diagnosis, financial crediting, etc. However,
current fairness research that primarily craft on i.i.d data, cannot be
trivially replicated to non-i.i.d. graph structures with topological dependence
among samples. Existing fair graph learning typically favors pairwise
constraints to achieve fairness but fails to cast off dimensional limitations
and generalize them into multiple sensitive attributes; besides, most studies
focus on in-processing techniques to enforce and calibrate fairness,
constructing a model-agnostic debiasing GNN framework at the pre-processing
stage to prevent downstream misuses and improve training reliability is still
largely under-explored. Furthermore, previous work on GNNs tend to enhance
either fairness or privacy individually but few probe into their interplays. In
this paper, we propose a novel model-agnostic debiasing framework named MAPPING
(\underline{M}asking \underline{A}nd \underline{P}runing and
Message-\underline{P}assing train\underline{ING}) for fair node classification,
in which we adopt the distance covariance($dCov$)-based fairness constraints to
simultaneously reduce feature and topology biases in arbitrary dimensions, and
combine them with adversarial debiasing to confine the risks of attribute
inference attacks. Experiments on real-world datasets with different GNN
variants demonstrate the effectiveness and flexibility of MAPPING. Our results
show that MAPPING can achieve better trade-offs between utility and fairness,
and mitigate privacy risks of sensitive information leakage.

提出了一种名为 MAPPING 的新型模型无关的去偏框架，用于公平节点分类，其中采用了基于距离协方差 $dCov$ 的公平约束，同时减小了任意维度中的特征和拓扑偏差，并结合对抗去偏以限制属性推断攻击的风险。在不同的 GNN 变体的真实数据集上进行实验，证明了 MAPPING 的有效性和灵活性，结果显示 MAPPING 能够在效用与公平之间取得更好的权衡，并减轻敏感信息泄露的隐私风险。