Collaborate to Adapt: Source-Free Graph Domain Adaptation via
  Bi-directional Adaptation
Zhen Zhang Meihan Liu Anhui Wang Hongyang Chen Zhao Li Jiajun Bu Bingsheng He
Unsupervised Graph Domain Adaptation (UGDA) has emerged as a practical
solution to transfer knowledge from a label-rich source graph to a completely
unlabelled target graph. However, most methods require a labelled source graph
to provide supervision signals, which might not be accessible in the real-world
settings due to regulations and privacy concerns. In this paper, we explore the
scenario of source-free unsupervised graph domain adaptation, which tries to
address the domain adaptation problem without accessing the labelled source
graph. Specifically, we present a novel paradigm called GraphCTA, which
performs model adaptation and graph adaptation collaboratively through a series
of procedures: (1) conduct model adaptation based on node's neighborhood
predictions in target graph considering both local and global information; (2)
perform graph adaptation by updating graph structure and node attributes via
neighborhood contrastive learning; and (3) the updated graph serves as an input
to facilitate the subsequent iteration of model adaptation, thereby
establishing a collaborative loop between model adaptation and graph
adaptation. Comprehensive experiments are conducted on various public datasets.
The experimental results demonstrate that our proposed model outperforms recent
source-free baselines by large margins.