Chuxun Liu

Learning Fair Graph Representations via Probability of Necessity and Sufficiency

Graph Neural Networks (GNNs) excel at modeling graph data but often amplify biases tied to sensitive attributes like gender and race. Existing causality-based methods use isolated interventions on graph topology or features but struggle to produce representations that balance predictive power with f