Graph Foundation Models: A Comprehensive Survey
Zehong Wang, Zheyuan Liu, Tianyi Ma, Jiazheng Li, Zheyuan Zhang, Xingbo Fu, Yiyang Li, Zhengqing Yuan, Wei Song, Yijun Ma, Qingkai Zeng, Xiusi Chen, Jianan Zhao, Jundong Li, Meng Jiang, Pietro Lio, Nitesh Chawla, Chuxu Zhang, Yanfang Ye

TL;DR
This survey reviews the development of Graph Foundation Models (GFMs), highlighting their architectures, pretraining, and adaptation strategies, and discusses their potential to enable scalable, general-purpose reasoning over structured graph data.
Contribution
It provides a comprehensive, modular overview of GFMs, categorizing them by scope, and discusses theoretical foundations, challenges, and future research directions.
Findings
GFMs unify diverse graph learning efforts under a modular framework.
The survey identifies key challenges like heterogeneity and scalability.
GFMs have potential to become foundational for reasoning over structured data.
Abstract
Graph-structured data pervades domains such as social networks, biological systems, knowledge graphs, and recommender systems. While foundation models have transformed natural language processing, vision, and multimodal learning through large-scale pretraining and generalization, extending these capabilities to graphs -- characterized by non-Euclidean structures and complex relational semantics -- poses unique challenges and opens new opportunities. To this end, Graph Foundation Models (GFMs) aim to bring scalable, general-purpose intelligence to structured data, enabling broad transfer across graph-centric tasks and domains. This survey provides a comprehensive overview of GFMs, unifying diverse efforts under a modular framework comprising three key components: backbone architectures, pretraining strategies, and adaptation mechanisms. We categorize GFMs by their generalization scope --…
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Taxonomy
TopicsAdvanced Graph Neural Networks · Model-Driven Software Engineering Techniques · Distributed and Parallel Computing Systems
