Dingying Xu

Heterogeneous fairness-aware adaptive feature enhancement framework for graph neural networks learning

Current Graph Neural Networks (GNNs), which predominantly rely on the homophily assumption, often amplify biases and degrade performance for minority groups in real-world graphs characterized by prevalent heterophilic structures. Current methods face limitations due to either dependence on sensitive attributes or separ...