Collaborate to Adapt: Source-Free Graph Domain Adaptation via Bi-directional Adaptation
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arXiv
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| Format: | Preprint |
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2024
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| _version_ | 1866910351117778944 |
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| author | Zhang, Zhen Liu, Meihan Wang, Anhui Chen, Hongyang Li, Zhao Bu, Jiajun He, Bingsheng |
| author_facet | Zhang, Zhen Liu, Meihan Wang, Anhui Chen, Hongyang Li, Zhao Bu, Jiajun He, Bingsheng |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2403_01467 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Collaborate to Adapt: Source-Free Graph Domain Adaptation via Bi-directional Adaptation Zhang, Zhen Liu, Meihan Wang, Anhui Chen, Hongyang Li, Zhao Bu, Jiajun He, Bingsheng Machine Learning Artificial Intelligence 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. |
| title | Collaborate to Adapt: Source-Free Graph Domain Adaptation via Bi-directional Adaptation |
| topic | Machine Learning Artificial Intelligence |
| url | https://arxiv.org/abs/2403.01467 |