We found a match
Your institution may have rights to this item. Sign in to continue.
- Title
Research Progress of Adversarial Defenses on Graphs.
- Authors
LI Penghui; ZHAI Zhengli; FENG Shu
- Abstract
Graph neural networks (GNN) have been successfully applied in complex tasks in many fields, but recent studies show that GNN is vulnerable to graph adversarial attacks, leading to severe performance degradation. The vulnerability of GNN affects all applications including node classification, link prediction and community detection. Graph adversarial attacks can be implemented efficiently, which brings serious security risks and privacy issues. Graph adversarial defense is dedicated to improving the robustness and generalization of GNN to resist adversarial attacks. Research progress of graph adversarial defense algorithm is reviewed. First, the work background and related concepts of graph adversarial defense are introduced, and the development process of graph adversarial defense is analyzed. Then, according to different defense strategies of the defense algorithm, the algorithms are divided into four categories, including attack detection, adversarial training, robustness certification and immunologic defense and the strategies of each type of defense algorithm are summarized. Furthermore, the principles and implementation of defense algorithm are analyzed, and typical algorithms are compared in terms of defense strategies, target task, advantages, disadvantages and experiments. Finally, through a comprehensive and systematic analysis of the existing graph adversarial defense algorithm, the problems and developing directions of the defense algorithm are summarized to provide help for further development of graph adversarial defense.
- Publication
Journal of Frontiers of Computer Science & Technology, 2021, Vol 15, Issue 12, p2292
- ISSN
1673-9418
- Publication type
Article
- DOI
10.3778/j.issn.1673-9418.2105021