Fair Graph Neural Network with Supervised Contrastive Regularization
Mahdi Tavassoli Kejani Fadi Dornaika Jean-Michel Loubes
In recent years, Graph Neural Networks (GNNs) have made significant
advancements, particularly in tasks such as node classification, link
prediction, and graph representation. However, challenges arise from biases
that can be hidden not only in the node attributes but also in the connections
between entities. Therefore, ensuring fairness in graph neural network learning
has become a critical problem. To address this issue, we propose a novel model
for training fairness-aware GNN, which enhances the Counterfactual Augmented
Fair Graph Neural Network Framework (CAF). Our approach integrates Supervised
Contrastive Loss and Environmental Loss to enhance both accuracy and fairness.
Experimental validation on three real datasets demonstrates the superiority of
our proposed model over CAF and several other existing graph-based learning
methods.