GJDNet: Robust Graph Neural Networks via Joint Disentangled Learning Against Adversarial Attacks 事件

GJDNet: Robust Graph Neural Networks via Joint Disentangled Learning Against Adversarial Attacks arXiv:2606.01560v1 Announce Type: cross Abstract: Graph Neural Networks (GNNs) are vulnerable to adversarial attacks, which inherently invert connectivity patterns by introducing disassortative edges in assortative graphs and assortative edges in disassortative graphs. This structural inversion creates structure-feature mismatches that disrupt neighborhood aggregation across different graph types. H