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arXiv · 2503.11086

A Survey of Cross-domain Graph Learning: Progress and Future Directions

Haihong Zhao·Zhixun Li·Chenyi Zi·Aochuan Chen·Fugee Tsung·Jia Li·Jeffrey Xu Yu
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Abstract

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.

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Haihong Zhao, Zhixun Li, Chenyi Zi, Aochuan Chen, Fugee Tsung, Jia Li, Jeffrey Xu Yu. 2025-03-14. A Survey of Cross-domain Graph Learning: Progress and Future Directions. https://arxiv.org/abs/2503.11086

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