3 papers
cs.LG2025
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…
cs.LG2024
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…