A Survey of Cross-domain Graph Learning: Progress and Future Directions
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| Main Authors: | , , , , , , |
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| Format: | Preprint |
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2025
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| _version_ | 1866918207566118912 |
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| author | Zhao, Haihong Li, Zhixun Zi, Chenyi Chen, Aochuan Tsung, Fugee Li, Jia Yu, Jeffrey Xu |
| author_facet | Zhao, Haihong Li, Zhixun Zi, Chenyi Chen, Aochuan Tsung, Fugee Li, Jia Yu, Jeffrey Xu |
| contents | Graph learning plays a vital role in mining and analyzing complex relationships within graph data and has been widely applied to real-world scenarios such as social, citation, and e-commerce networks. Foundation models in computer vision (CV) and natural language processing (NLP) have demonstrated remarkable cross-domain capabilities that are equally significant for graph data. However, existing graph learning approaches often struggle to generalize across domains. Motivated by recent advances in CV and NLP, cross-domain graph learning (CDGL) has gained renewed attention as a promising step toward realizing true graph foundation models. In this survey, we provide a comprehensive review and analysis of existing works on CDGL. We propose a new taxonomy that categorizes existing approaches according to the type of transferable knowledge learned across domains: structure-oriented, feature-oriented, and mixture-oriented. Based on this taxonomy, we systematically summarize representative methods in each category, discuss the key challenges and limitations of current studies, and outline promising directions for future research. A continuously updated collection of related works is available at: https://github.com/cshhzhao/Awesome-Cross-Domain-Graph-Learning. |
| format | Preprint |
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arxiv_https___arxiv_org_abs_2503_11086 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | A Survey of Cross-domain Graph Learning: Progress and Future Directions Zhao, Haihong Li, Zhixun Zi, Chenyi Chen, Aochuan Tsung, Fugee Li, Jia Yu, Jeffrey Xu Machine Learning Artificial Intelligence Graph learning plays a vital role in mining and analyzing complex relationships within graph data and has been widely applied to real-world scenarios such as social, citation, and e-commerce networks. Foundation models in computer vision (CV) and natural language processing (NLP) have demonstrated remarkable cross-domain capabilities that are equally significant for graph data. However, existing graph learning approaches often struggle to generalize across domains. Motivated by recent advances in CV and NLP, cross-domain graph learning (CDGL) has gained renewed attention as a promising step toward realizing true graph foundation models. In this survey, we provide a comprehensive review and analysis of existing works on CDGL. We propose a new taxonomy that categorizes existing approaches according to the type of transferable knowledge learned across domains: structure-oriented, feature-oriented, and mixture-oriented. Based on this taxonomy, we systematically summarize representative methods in each category, discuss the key challenges and limitations of current studies, and outline promising directions for future research. A continuously updated collection of related works is available at: https://github.com/cshhzhao/Awesome-Cross-Domain-Graph-Learning. |
| title | A Survey of Cross-domain Graph Learning: Progress and Future Directions |
| topic | Machine Learning Artificial Intelligence |
| url | https://arxiv.org/abs/2503.11086 |