5 citations · 8 across the 5 of their papers we have counts for
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cs.LG2024★ 3 cited
GISExplainer: On Explainability of Graph Neural Networks via Game-theoretic Interaction Subgraphs
Xingping Xian, Jianlu Liu, Chao Wang +4
Explainability is crucial for the application of black-box Graph Neural Networks (GNNs) in critical fields such as healthcare, finance, cybersecurity, and more. Various feature att…
cs.SI2024
GraphMU: Repairing Robustness of Graph Neural Networks via Machine Unlearning
Tao Wu, Xinwen Cao, Chao Wang +5
Graph Neural Networks (GNNs) have demonstrated significant application potential in various fields. However, GNNs are still vulnerable to adversarial attacks. Numerous adversarial…
cs.LG2024
Understanding the Robustness of Graph Neural Networks against Adversarial Attacks
Tao Wu, Canyixing Cui, Xingping Xian +4
Recent studies have shown that graph neural networks (GNNs) are vulnerable to adversarial attacks, posing significant challenges to their deployment in safety-critical scenarios. T…