Collaborate to Adapt: Source-Free Graph Domain Adaptation via Bi-directional Adaptation

• URL: http://arxiv.org/abs/2403.01467v1
• Date: Sun, 3 Mar 2024 10:23:08 GMT
• Title: Collaborate to Adapt: Source-Free Graph Domain Adaptation via Bi-directional Adaptation
• Authors: Zhen Zhang, Meihan Liu, Anhui Wang, Hongyang Chen, Zhao Li, Jiajun Bu, Bingsheng He
• Abstract summary: 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. We present a novel paradigm called GraphCTA, which performs model adaptation and graph adaptation collaboratively. Our proposed model outperforms recent source-free baselines by large margins.
• Score: 40.25858820407687
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: 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.

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