  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.
