Homophily-aware Supervised Contrastive Counterfactual Augmented Fair Graph Neural Network

ArXi:2604.02342v1 Announce Type: new In recent years, Graph Neural Networks (GNNs) have achieved remarkable success in tasks such as node classification, link prediction, and graph representation learning. However, they remain susceptible to biases that can arise not only from node attributes but also from the graph structure itself. Addressing fairness in GNNs has therefore emerged as a critical research challenge. In this work, we propose a novel model for