Learning Counterfactual Fairness from Authentic Generation
Graph Counterfactual Fairness (GCFair), a novel framework that achieves counterfactual fairness by explicitly identifying and disentangling the subsets of node features and graph structures genuinely affected by sensitive attributes, is proposed.
Zichong Wang, Zhipeng Yin, Zhong Chen et al.
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