Contrastive Adversarial Training for Robust Graph Neural Networks under Label Poisoning.

arXiv:2610.24685v1 Announce Type: cross Abstract: Graph Neural Networks (GNNs) are effective for modeling relational data but are vulnerable to label poisoning, where a small number of corrupted training labels can propagate errors across the graph via message