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.

翻译：图能够帮助建模各种复杂系统（如基因网络和电网）并分析其中的潜在关系。近年来，基于图的学习日益受到关注，尤其是基于图神经网络（GNN）的方法，其中图注意力网络（GATs）已成为最广泛使用的图任务神经网络结构之一。尽管已有研究表明，利用图结构进行学习会放大算法偏差，但GAT中注意力设计对算法偏差的影响尚未得到探讨。受此启发，本研究首先进行理论分析，以证明基于GAT的节点分类学习中算法偏差的来源。随后，基于理论发现，提出了一种新算法FairGAT，该算法采用公平感知的注意力设计。在真实网络上的实验结果表明，FairGAT在提升群体公平性指标的同时，其节点分类和链路预测的性能与公平感知基线方法相当。