A GNN fairness model edits graphs for higher class homophily and lower sensitive-attribute homophily, then trains with supervised contrastive and environmental losses to improve both accuracy and fairness metrics over prior CAF baselines.
Fairdrop: Biased edge dropout for enhancing fairness in graph representation learning.IEEE Transactions on Artificial Intelligence, 3(3):344–354, 2021
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Homophily-aware Supervised Contrastive Counterfactual Augmented Fair Graph Neural Network
A GNN fairness model edits graphs for higher class homophily and lower sensitive-attribute homophily, then trains with supervised contrastive and environmental losses to improve both accuracy and fairness metrics over prior CAF baselines.