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cs.LG2025
JANUS: A Dual-Constraint Generative Framework for Stealthy Node Injection Attacks
Jiahao Zhang, Xiaobing Pei, Zhaokun Zhong +2
Graph Neural Networks (GNNs) have demonstrated remarkable performance across various applications, yet they are vulnerable to sophisticated adversarial attacks, particularly node i…
cs.LG2025
Out-of-Distribution Graph Models Merging
Yidi Wang, Ziyue Qiao, Jiawei Gu +4
This paper studies a novel problem of out-of-distribution graph models merging, which aims to construct a generalized model from multiple graph models pre-trained on different doma…
cs.LG2025
Revisiting the Relationship between Adversarial and Clean Training: Why Clean Training Can Make Adversarial Training Better
MingWei Zhou, Xiaobing Pei
Adversarial training (AT) is an effective technique for enhancing adversarial robustness, but it usually comes at the cost of a decline in generalization ability. Recent studies ha…