2 papers
cs.LG2024
On the Robustness of Graph Reduction Against GNN Backdoor
Yuxuan Zhu, Michael Mandulak, Kerui Wu +4
Graph Neural Networks (GNNs) are gaining popularity across various domains due to their effectiveness in learning graph-structured data. Nevertheless, they have been shown to be su…
cs.CR2024
E-SAGE: Explainability-based Defense Against Backdoor Attacks on Graph Neural Networks
Dingqiang Yuan, Xiaohua Xu, Lei Yu +3
Graph Neural Networks (GNNs) have recently been widely adopted in multiple domains. Yet, they are notably vulnerable to adversarial and backdoor attacks. In particular, backdoor at…