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.

翻译：随着机器学习中公平性问题的日益关注以及图神经网络在图数据学习中的卓越表现，图神经网络的算法公平性已引起广泛关注。现有许多研究在群体层面提升公平性，但仅有少量工作致力于促进个体公平性（即对相似个体产生相似结果）。一个理想的个体公平性框架应满足：（1）平衡公平性与性能；（2）兼容两种常用的个体相似性度量方法（外部标注与输入特征计算）；（3）可泛化至多种图神经网络模型；（4）计算高效。然而，现有研究均未能同时实现上述目标。本文提出一种新方法GFairHint，通过促进图神经网络的个体公平性同时实现上述所有目标。GFairHint通过辅助的链接预测任务学习公平性表征，并将该表征与原始图神经网络中学习的节点嵌入拼接作为"公平性提示"。在五种真实图数据集上，基于三种主流图神经网络模型（涵盖上述两种个体相似性度量）的广泛实验表明，GFairHint在几乎所有数据集与骨干模型的组合中均取得了最优公平性结果，同时生成可比的效用结果，且计算成本较先前最优方法大幅降低。