Universal Graph Backdoor Defense: A Feature-based Homophily Perspective
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. Our empirical results reveal that such structure-centric approaches fail to defend against emerging feature-based GBAs that preserve graph topology. Therefore, in this paper, we study a novel problem of universal graph backdoor defense. First, we investigate...
| Authors | Mengting Pan, Fan Li, Chen Chen et al. |
|---|---|
| Primary category | cs.CR |
| Published | 16 May 2026 |
| Last updated at source | 25 May 2026 |
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