Contrastive learning for fair graph representations via counterfactual graph augmentation

Graph neural networks (GNNs) have exhibited excellent performance in graph representation learning. However, GNNs might inherit biases from the data, leading to discriminatory predictions. Existing study mainly concentrates on attaining fairness through counterfactuals related to node attributes, overlooking the causal impact of bias in the graph structure. Herein, we introduce a novel framework called fair contrastive learning based on counterfactual graph augmentation (FCLCA), aimed at learning counterfactual fairness by mitigating graph structure bias. FCLCA first generates two counterfactual graphs through structural augmentation. Next, we maximize the consistency between representations produced by nodes in these two counterfactual graphs using contrastive learning. In addition, FCLCA uses adversarial debiasing learning to further reduce the influence of sensitive attributes on the learned node representations. Finally, an optimized training strategy is used for contrastive learning to enhance the learning of counterfactual fairness. Comprehensive experiments conducted on four real-world datasets proved the effectiveness of FCLCA in balancing classification performance and fairness.