Related papers: A Survey of Adversarial Learning on Graphs

A Survey of Adversarial Learning on Graphs

URL: http://arxiv.org/abs/2003.05730v3
Date: Tue, 5 Apr 2022 12:54:56 GMT
Title: A Survey of Adversarial Learning on Graphs
Authors: Liang Chen, Jintang Li, Jiaying Peng, Tao Xie, Zengxu Cao, Kun Xu, Xiangnan He, Zibin Zheng, Bingzhe Wu
Abstract summary: We investigate and summarize the existing works on graph adversarial learning tasks. Specifically, we survey and unify the existing works w.r.t. attack and defense in graph analysis tasks. We emphasize the importance of related evaluation metrics, investigate and summarize them comprehensively.
Score: 59.21341359399431
License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
Abstract: Deep learning models on graphs have achieved remarkable performance in various graph analysis tasks, e.g., node classification, link prediction, and graph clustering. However, they expose uncertainty and unreliability against the well-designed inputs, i.e., adversarial examples. Accordingly, a line of studies has emerged for both attack and defense addressed in different graph analysis tasks, leading to the arms race in graph adversarial learning. Despite the booming works, there still lacks a unified problem definition and a comprehensive review. To bridge this gap, we investigate and summarize the existing works on graph adversarial learning tasks systemically. Specifically, we survey and unify the existing works w.r.t. attack and defense in graph analysis tasks, and give appropriate definitions and taxonomies at the same time. Besides, we emphasize the importance of related evaluation metrics, investigate and summarize them comprehensively. Hopefully, our works can provide a comprehensive overview and offer insights for the relevant researchers. Latest advances in graph adversarial learning are summarized in our GitHub repository https://github.com/EdisonLeeeee/Graph-Adversarial-Learning.

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