Zhenqiang Ye

FairGSE: Fairness-Aware Graph Neural Network Without High False Positive Rates

Graph neural networks (GNNs) have emerged as the mainstream paradigm for graph representation learning due to their effective message aggregation. However, this advantage also amplifies biases inherent in graph topology, raising fairness concerns. Existing fairness-aware GNNs provide satisfactory pe