  The design of Graph Transformers (GTs) generally neglects considerations for
fairness, resulting in biased outcomes against certain sensitive subgroups.
Since GTs encode graph information without relying on message-passing
mechanisms, conventional fairness-aware graph learning methods cannot be
directly applicable to address these issues. To tackle this challenge, we
propose FairGT, a Fairness-aware Graph Transformer explicitly crafted to
mitigate fairness concerns inherent in GTs. FairGT incorporates a meticulous
structural feature selection strategy and a multi-hop node feature integration
method, ensuring independence of sensitive features and bolstering fairness
considerations. These fairness-aware graph information encodings seamlessly
integrate into the Transformer framework for downstream tasks. We also prove
that the proposed fair structural topology encoding with adjacency matrix
eigenvector selection and multi-hop integration are theoretically effective.
Empirical evaluations conducted across five real-world datasets demonstrate
FairGT's superiority in fairness metrics over existing graph transformers,
graph neural networks, and state-of-the-art fairness-aware graph learning
approaches.
