Graph Domain Adaptation via Homophily-Agnostic Reconstructing Structure
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arXiv
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| Hauptverfasser: | , , , , , , , , , , |
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| Format: | Preprint |
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2026
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| author | Fang, Ruiyi Wang, Shuo Pu, Ruizhi Zeng, Qiuhao Zheng, Hao Wang, Ziyan Cai, Jiale Mei, Zhimin Tang, Song Ling, Charles Wang, Boyu |
| author_facet | Fang, Ruiyi Wang, Shuo Pu, Ruizhi Zeng, Qiuhao Zheng, Hao Wang, Ziyan Cai, Jiale Mei, Zhimin Tang, Song Ling, Charles Wang, Boyu |
| contents | Graph Domain Adaptation (GDA) transfers knowledge from labeled source graphs to unlabeled target graphs, addressing the challenge of label scarcity. However, existing GDA methods typically assume that both source and target graphs exhibit homophily, leading existing methods to perform poorly when heterophily is present. Furthermore, the lack of labels in the target graph makes it impossible to assess its homophily level beforehand. To address this challenge, we propose a novel homophily-agnostic approach that effectively transfers knowledge between graphs with varying degrees of homophily. Specifically, we adopt a divide-and-conquer strategy that first separately reconstructs highly homophilic and heterophilic variants of both the source and target graphs, and then performs knowledge alignment separately between corresponding graph variants. Extensive experiments conducted on five benchmark datasets demonstrate the superior performance of our approach, particularly highlighting its substantial advantages on heterophilic graphs. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2602_07573 |
| institution | arXiv |
| publishDate | 2026 |
| record_format | arxiv |
| spellingShingle | Graph Domain Adaptation via Homophily-Agnostic Reconstructing Structure Fang, Ruiyi Wang, Shuo Pu, Ruizhi Zeng, Qiuhao Zheng, Hao Wang, Ziyan Cai, Jiale Mei, Zhimin Tang, Song Ling, Charles Wang, Boyu Social and Information Networks Artificial Intelligence Graph Domain Adaptation (GDA) transfers knowledge from labeled source graphs to unlabeled target graphs, addressing the challenge of label scarcity. However, existing GDA methods typically assume that both source and target graphs exhibit homophily, leading existing methods to perform poorly when heterophily is present. Furthermore, the lack of labels in the target graph makes it impossible to assess its homophily level beforehand. To address this challenge, we propose a novel homophily-agnostic approach that effectively transfers knowledge between graphs with varying degrees of homophily. Specifically, we adopt a divide-and-conquer strategy that first separately reconstructs highly homophilic and heterophilic variants of both the source and target graphs, and then performs knowledge alignment separately between corresponding graph variants. Extensive experiments conducted on five benchmark datasets demonstrate the superior performance of our approach, particularly highlighting its substantial advantages on heterophilic graphs. |
| title | Graph Domain Adaptation via Homophily-Agnostic Reconstructing Structure |
| topic | Social and Information Networks Artificial Intelligence |
| url | https://arxiv.org/abs/2602.07573 |