  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.
