ERGCN: Data enhancement-based robust graph convolutional network against adversarial attacks. - Tao Wu 003; Nan Yang; Long Chen; Xiaokui Xiao; Xingping Xian; Jun Liu 044; Shaojie Qiao; Canyixing Cui (Inf. Sci., 2022)

GraphMU: Repairing Robustness of Graph Neural Networks via Machine Unlearning. - Tao Wu 003; Xinwen Cao; Chao Wang 025; Shaojie Qiao; Xingping Xian; Lin Yuan 002; Canyixing Cui; Yanbing Liu (CoRR, 2024)

Explainable AI Security: Exploring Robustness of Graph Neural Networks to Adversarial Attacks. - Tao Wu 003; Canyixing Cui; Xingping Xian; Shaojie Qiao; Chao Wang 025; Lin Yuan 002; Shui Yu 001 (CoRR, 2024)

Understanding the robustness of graph neural networks against adversarial attacks. - Tao Wu 003; Canyixing Cui; Xingping Xian; Shaojie Qiao; Chao Wang 025; Lin Yuan 002; Shui Yu 001 (Knowl. Based Syst., 2025)

Disentangled Graph Representation Based on Substructure-Aware Graph Optimal Matching Kernel Convolutional Networks. - Mao Wang; Tao Wu 003; Xingping Xian; Shaojie Qiao; Weina Niu; Canyixing Cui (CoRR, 2025)