  Graphs can facilitate modeling various complex systems such as gene networks
and power grids, as well as analyzing the underlying relations within them.
Learning over graphs has recently attracted increasing attention, particularly
graph neural network-based (GNN) solutions, among which graph attention
networks (GATs) have become one of the most widely utilized neural network
structures for graph-based tasks. Although it is shown that the use of graph
structures in learning results in the amplification of algorithmic bias, the
influence of the attention design in GATs on algorithmic bias has not been
investigated. Motivated by this, the present study first carries out a
theoretical analysis in order to demonstrate the sources of algorithmic bias in
GAT-based learning for node classification. Then, a novel algorithm, FairGAT,
that leverages a fairness-aware attention design is developed based on the
theoretical findings. Experimental results on real-world networks demonstrate
that FairGAT improves group fairness measures while also providing comparable
utility to the fairness-aware baselines for node classification and link
prediction.
