  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.
