Adversarial Attack and Defense on Graph Data: A Survey. (arXiv:1812.10528v1 [cs.CR])

Deep neural networks (DNNs) have been widely applied in various applications involving image, text, audio, and graph data. However, recent studies have shown that DNNs are vulnerable to adversarial attack. Though there are several works studying adversarial attack and defense on domains such as images and text processing, it is difficult to directly transfer the learned knowledge to graph data due to its representation challenge. Given the importance of graph analysis, increasing number of works start to analyze the robustness of machine learning models on graph. Nevertheless, current studies considering adversarial behaviors on graph data usually focus on specific types of attacks with certain assumptions. In addition, each work proposes its own mathematical formulation which makes the comparison among different methods difficult. Therefore, in this paper, we aim to survey existing adversarial attack strategies on graph data and provide an unified problem formulation which can cover all current adversarial learning studies on graph. We also compare different attacks on graph data and discuss their corresponding contributions and limitations. Finally, we discuss several future research directions in this area.

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