The paper proposes a universal defense against subgraph-based and feature-based graph backdoor attacks on GNNs by exploiting lower feature-based homophily in backdoored nodes via neighbor-aware reconstruction loss and robust training.
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Universal Graph Backdoor Defense: A Feature-based Homophily Perspective
The paper proposes a universal defense against subgraph-based and feature-based graph backdoor attacks on GNNs by exploiting lower feature-based homophily in backdoored nodes via neighbor-aware reconstruction loss and robust training.