ResGAT

This paper proposes ResGAT, a method based on residual graph attention network and pairwise AUC loss, which effectively addresses the three major challenges of deep feature propagation, sample imbalance, and ranking optimization through residual connections, hard negative sampling, and pairwise AUC loss, achieving superior AUC and AUPR performance over existing state-of-the-art methods on four public benchmark datasets.