[2604.02342] Homophily-aware Supervised Contrastive Counterfactual Augmented Fair Graph Neural Network

Computer Science > Machine Learning arXiv:2604.02342 (cs) [Submitted on 8 Feb 2026] Title:Homophily-aware Supervised Contrastive Counterfactual Augmented Fair Graph Neural Network Authors:Mahdi Tavassoli Kejani, Fadi Dornaika, Charlotte Laclau, Jean-Michel Loubes View a PDF of the paper titled Homophily-aware Supervised Contrastive Counterfactual Augmented Fair Graph Neural Network, by Mahdi Tavassoli Kejani and 3 other authors View PDF HTML (experimental) Abstract: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 training fairness-aware GNNs by improving the counterfactual augmented fair graph neural network framework (CAF). Specifically, our approach introduces a two-phase training strategy: in the first phase, we edit the graph to increase homophily ratio with respect to class labels while reducing homophily ratio with respect to sensitive attribute labels; in the second phase, we integrate a modified supervised contrastive loss and environmental loss into the optimization process, enabling the model to jointly improve predictive performance and fairness. Experiments on five real-world datasets demons...