Collaborate to Adapt: Source-Free Graph Domain Adaptation via Bi-directional Adaptation
<|reference_start|>Collaborate to Adapt: Source-Free Graph Domain Adaptation via Bi-directional Adaptation: 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.<|reference_end|>
@article{zhang2024collaborate,
    title={Collaborate to Adapt: Source-Free Graph Domain Adaptation via
  Bi-directional Adaptation},
    author={Zhen Zhang, Meihan Liu, Anhui Wang, Hongyang Chen, Zhao Li, Jiajun Bu,
  Bingsheng He},
    journal={arXiv preprint arXiv:2403.01467},
    year={2024},
    doi={10.1145/3589334.3645507},
    archivePrefix={arXiv},
    eprint={2403.01467},
    primaryClass={cs.LG cs.AI}
}