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.

翻译：摘要：尽管图神经网络（GNNs）在各种网络应用中取得了显著成功，但它们继承并进一步加剧了历史歧视和社会刻板印象，这严重阻碍了其在在线临床诊断、金融信贷等高风险领域的部署。然而，当前主要基于独立同分布数据设计的公平性研究无法简单推广到样本间存在拓扑依赖的非独立同分布图结构。现有公平图学习通常采用成对约束实现公平性，但难以突破维度限制并将其推广至多个敏感属性；此外，多数研究聚焦于处理阶段技术以强制执行和校准公平性，而在预处理阶段构建模型无关的去偏GNN框架以预防下游误用并提升训练可靠性仍处于探索不足的状态。更关键的是，现有GNN研究往往单独提升公平性或隐私性，鲜有探究两者的交互作用。本文提出一种名为MAPPING（掩码-剪枝-消息传递训练）的创新模型无关去偏框架，用于公平节点分类。该框架采用基于距离协方差（$dCov$）的公平约束，可在任意维度下同步降低特征偏差和拓扑偏差，并将其与对抗去偏相结合以限制属性推断攻击风险。在真实数据集上使用不同GNN变体开展的实验验证了MAPPING的有效性与灵活性。结果表明，MAPPING能在效用与公平性之间实现更优权衡，并有效缓解敏感信息泄露带来的隐私风险。