Graph Foundation Models: Challenges, Methods, and Open Questions

Foundation models have revolutionized machine learning by enabling general-purpose reasoning across diverse tasks and domains. These models, pretrained on large-scale data, demonstrate strong adaptability with minimal task-specific supervision, leading to breakthroughs in natural language processing and computer vision. Inspired by this paradigm, Graph Foundation Models (GFMs) have emerged to extend the benefits of foundation models to graph-structured data, which is pretrained on massive graphs and can be fast adapted to different downstream tasks. In this paper, we provide a comprehensive survey of the state-of-the-art techniques of graph foundation models. In particular, we (1) formally categorize the challenges in designing graph foundation models; (2) comprehensively review the existing and recent advances of graph foundation models; (3) extend the graph foundation models in real-world problems; and (4) elucidate open questions and future research directions. Our systematic review summarizes representative models, highlights key design principles, and provides comparative analyses. This survey introduces major topics within foundation models and offers a guide to a new frontier of graph learning. Our extended survey is available at https://arxiv.org/abs/2505.15116.