Graph neural networks (GNNs) have emerged as a powerful tool for analyzing and learning from complex data structured as graphs, demonstrating remarkable effectiveness in various applications, such as social network analysis, recommendation systems, and drug discovery. However, despite their impressive performance, the fairness problem has increasingly gained attention as a crucial aspect to consider. Existing research in graph learning focuses on either group fairness or individual fairness. However, since each concept provides unique insights into fairness from distinct perspectives, integrating them into a fair graph neural network system is crucial. To the best of our knowledge, no study has yet to comprehensively tackle both individual and group fairness simultaneously. In this paper, we propose a new concept of individual fairness within groups and a novel framework named Fairness for Group and Individual (FairGI), which considers both group fairness and individual fairness within groups in the context of graph learning. FairGI employs the similarity matrix of individuals to achieve individual fairness within groups, while leveraging adversarial learning to address group fairness in terms of both Equal Opportunity and Statistical Parity. The experimental results demonstrate that our approach not only outperforms other state-of-the-art models in terms of group fairness and individual fairness within groups, but also exhibits excellent performance in population-level individual fairness, while maintaining comparable prediction accuracy.

翻译：图神经网络（GNN）已成为分析学习图结构复杂数据的强大工具，在社交网络分析、推荐系统和药物发现等多个领域展现出卓越效能。然而，尽管性能出众，公平性问题作为关键考量因素日益得到关注。现有图学习研究聚焦于群体公平性或个体公平性，但由于这两个概念从不同维度提供独特的公平性视角，将其整合至公平的图神经网络系统至关重要。据我们所知，尚无研究同时全面解决个体公平性与群体公平性问题。本文提出群内个体公平性新概念及名为“群体与个体公平性”（FairGI）的创新框架，该框架在图学习场景中同时兼顾群体公平性与群内个体公平性。FairGI通过个体相似性矩阵实现群内个体公平性，同时利用对抗学习从机会均等与统计均等两个维度解决群体公平性问题。实验结果表明，我们的方法不仅在群体公平性和群内个体公平性方面优于其他最先进模型，在种群级个体公平性方面也表现出色，同时保持了可比的预测精度。