AI RESEARCH
Universal Graph Backdoor Defense: A Feature-based Homophily Perspective
arXiv CS.LG
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ArXi:2605.16815v1 Announce Type: cross Graph neural networks (GNNs) have achieved remarkable success in relational learning. However, their vulnerability to graph backdoor attacks (GBAs) poses a significant barrier to broader adoption in high-stakes applications. Despite recent advances in graph backdoor defense (GBD), existing methods primarily focus on subgraph-based GBAs, relying on the assumption that poisoned target nodes are explicitly connected to subgraph triggers.