5 papers · 1 filter
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…
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…
AHSG: Adversarial Attack on High-level Semantics in Graph Neural Networks
Kai Yuan, Jiahao Zhang, Yidi Wang +1
Adversarial attacks on Graph Neural Networks aim to perturb the performance of the learner by carefully modifying the graph topology and node attributes. Existing methods achieve a…
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…
IENE: Identifying and Extrapolating the Node Environment for Out-of-Distribution Generalization on Graphs
Haoran Yang, Xiaobing Pei, Kai Yuan
Due to the performance degradation of graph neural networks (GNNs) under distribution shifts, the work on out-of-distribution (OOD) generalization on graphs has received widespread…