Adversarial attack and defense on graph neural networks: a survey
CHEN Jinyin,
ZHANG Dunjie, HUANG Guohan, LIN Xiang,
BAO Liang
Affiliations
CHEN Jinyin
Institute of Cyberspace Security, Zhejiang University of Technology, Hangzhou 310023, China ;The College of Information Engineering, Zhejiang University of Technology, Hangzhou 310023, China
ZHANG Dunjie, HUANG Guohan, LIN Xiang
The College of Information Engineering, Zhejiang University of Technology, Hangzhou 310023, China
BAO Liang
Key Lab of Information Network Security, Ministry of Public Security, Shanghai 200000, China
For the numerous existing adversarial attack and defense methods on GNN, the main adversarial attack and defense algorithms of GNN were reviewed comprehensively, as well as robustness analysis techniques. Besides, the commonly used benchmark datasets and evaluation metrics in the security research of GNN were introduced. In conclusion, some insights on the future research direction of adversarial attacks and the trend of development were put forward.
