Abstract: Federated graph attention networks (FGATs) are gaining prominence for enabling collaborative and privacy-preserving graph model training. The attention mechanisms in FGATs enhance the focus ...
Abstract: Graph contrastive learning (GCL) has achieved remarkable success in graph self-supervised learning (SSL) through an augmenting-contrasting paradigm. Existing augmentation strategies ...