  Given the growing concerns about fairness in machine learning and the
impressive performance of Graph Neural Networks (GNNs) on graph data learning,
algorithmic fairness in GNNs has attracted significant attention. While many
existing studies improve fairness at the group level, only a few works promote
individual fairness, which renders similar outcomes for similar individuals. A
desirable framework that promotes individual fairness should (1) balance
between fairness and performance, (2) accommodate two commonly-used individual
similarity measures (externally annotated and computed from input features),
(3) generalize across various GNN models, and (4) be computationally efficient.
Unfortunately, none of the prior work achieves all the desirables. In this
work, we propose a novel method, GFairHint, which promotes individual fairness
in GNNs and achieves all aforementioned desirables. GFairHint learns fairness
representations through an auxiliary link prediction task, and then
concatenates the representations with the learned node embeddings in original
GNNs as a "fairness hint". Through extensive experimental investigations on
five real-world graph datasets under three prevalent GNN models covering both
individual similarity measures above, GFairHint achieves the best fairness
results in almost all combinations of datasets with various backbone models,
while generating comparable utility results, with much less computational cost
compared to the previous state-of-the-art (SoTA) method.
