  As graph representation learning often suffers from label scarcity problems
in real-world applications, researchers have proposed graph domain adaptation
(GDA) as an effective knowledge-transfer paradigm across graphs. In particular,
to enhance model performance on target graphs with specific tasks, GDA
introduces a bunch of task-related graphs as source graphs and adapts the
knowledge learnt from source graphs to the target graphs. Since GDA combines
the advantages of graph representation learning and domain adaptation, it has
become a promising direction of transfer learning on graphs and has attracted
an increasing amount of research interest in recent years. In this paper, we
comprehensively overview the studies of GDA and present a detailed survey of
recent advances. Specifically, we outline the research status and challenges,
propose a taxonomy, introduce the details of representative works, and discuss
the prospects. To the best of our knowledge, this paper is the first survey for
graph domain adaptation. A detailed paper list is available at
https://github.com/Skyorca/Awesome-Graph-Domain-Adaptation-Papers.
