梳理跨领域图学习进展，为构建通用图模型指明方向
A Survey of Cross-domain Graph Learning: Progress and Future Directions
- 按可迁移知识类型分为结构、特征、混合三类，构建新分类体系
- 系统总结代表性方法，揭示当前研究在泛化能力上的瓶颈
- 适合关注图神经网络泛化与基础模型的研究者阅读
图学习在挖掘和分析图数据中的复杂关系中发挥着关键作用，已广泛应用于社交、引用和电商网络等真实场景。计算机视觉（CV）和自然语言处理（NLP）领域的基础模型展现出显著的跨域能力，这对图数据同样重要。然而，现有图学习方法在跨域泛化方面仍面临挑战。受CV和NLP领域最新进展启发，跨领域图学习（CDGL）成为实现真正图基础模型的有前景方向。本文全面综述现有CDGL研究，提出一种新分类体系，根据跨域可迁移知识类型将方法分为结构导向、特征导向和混合导向三类。基于该分类，系统总结各类别代表性方法，讨论当前研究的关键挑战与局限，并展望未来有潜力的研究方向。相关工作持续更新集合见：https://github.com/cshhzhao/Awesome-Cross-Domain-Graph-Learning。
原文摘要 · Abstract (English)
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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