A Survey of Adversarial Learning on Graph
Deep learning models on graphs have achieved remarkable performance in various graph analysis tasks, e.g. node classication, 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 have emerged for both aack and defense addressed in dierent graph analysis tasks, leading to the arms race in graph adversarial learning. Despite the booming works, there still lacks a unied problem denition and a comprehensive review. To bridge this gap, we investigate and summarize the existing works on graph adversarial learning tasks systemically. Specically, we survey and unify the existing works w.r.t. aack and defense in graph analysis tasks, and give appropriate denitions 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 oer insights for the relevant researchers. More details of our works are available at hps:/github.com/gitgiter/Graph Adversarial Learning.