  Deep neural networks (DNNs) have been widely applied to various applications,
including image classification, text generation, audio recognition, and graph
data analysis. However, recent studies have shown that DNNs are vulnerable to
adversarial attacks. Though there are several works about adversarial attack
and defense strategies on domains such as images and natural language
processing, it is still difficult to directly transfer the learned knowledge to
graph data due to its representation structure. Given the importance of graph
analysis, an increasing number of studies over the past few years have
attempted to analyze the robustness of machine learning models on graph data.
Nevertheless, existing research considering adversarial behaviors on graph data
often focuses 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, this review is
intended to provide an overall landscape of more than 100 papers on adversarial
attack and defense strategies for graph data, and establish a unified
formulation encompassing most graph adversarial learning models. Moreover, we
also compare different graph attacks and defenses along with their
contributions and limitations, as well as summarize the evaluation metrics,
datasets and future trends. We hope this survey can help fill the gap in the
literature and facilitate further development of this promising new field.
