  Recent studies have highlighted fairness issues in Graph Neural Networks
(GNNs), where they produce discriminatory predictions against specific
protected groups categorized by sensitive attributes such as race and age.
While various efforts to enhance GNN fairness have made significant progress,
these approaches are often tailored to specific sensitive attributes.
Consequently, they necessitate retraining the model from scratch to accommodate
changes in the sensitive attribute requirement, resulting in high computational
costs. To gain deeper insights into this issue, we approach the graph fairness
problem from a causal modeling perspective, where we identify the confounding
effect induced by the sensitive attribute as the underlying reason. Motivated
by this observation, we formulate the fairness problem in graphs from an
invariant learning perspective, which aims to learn invariant representations
across environments. Accordingly, we propose a graph fairness framework based
on invariant learning, namely FairINV, which enables the training of fair GNNs
to accommodate various sensitive attributes within a single training session.
Specifically, FairINV incorporates sensitive attribute partition and trains
fair GNNs by eliminating spurious correlations between the label and various
sensitive attributes. Experimental results on several real-world datasets
demonstrate that FairINV significantly outperforms state-of-the-art fairness
approaches, underscoring its effectiveness. Our code is available via:
https://github.com/ZzoomD/FairINV/.
