# Graph Domain Adaptation: Challenges, Progress and Prospects


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.

[Read Full Article](https://arxiv.org/abs/2402.00904)