Emerging as fundamental building blocks for diverse artificial intelligence applications, foundation models have achieved notable success across natural language processing and many other domains. Parallelly, graph machine learning has witnessed a transformative shift, with shallow methods giving way to deep learning approaches. The emergence and homogenization capabilities of foundation models have piqued the interest of graph machine learning researchers, sparking discussions about developing the next graph learning paradigm that is pre-trained on broad graph data and can be adapted to a wide range of downstream graph tasks. However, there is currently no clear definition and systematic analysis for this type of work. In this article, we propose the concept of graph foundation models (GFMs), and provide the first comprehensive elucidation on their key characteristics and technologies. Following that, we categorize existing works towards GFMs into three categories based on their reliance on graph neural networks and large language models. Beyond providing a comprehensive overview of the current landscape of graph foundation models, this article also discusses potential research directions for this evolving field.

翻译：作为多样化人工智能应用的基本构建模块，基础模型已在自然语言处理及众多其他领域取得了显著成功。与此同时，图机器学习正经历深刻变革，浅层方法逐渐被深度学习方法取代。基础模型的出现及其同质化能力引发了图机器学习研究者的兴趣，促使他们探讨开发下一代图学习范式——该范式在海量图数据上预训练，并可适应广泛的下游图任务。然而，目前对此类工作缺乏清晰定义和系统分析。本文提出图基础模型（Graph Foundation Models, GFMs）的概念，并首次全面阐明其关键特征与核心技术。在此基础上，我们将现有面向GFMs的工作分为三类：基于图神经网络、基于大规模语言模型，以及两者融合的方法。除系统概述图基础模型的研究现状外，本文还探讨了这一新兴领域的潜在研究方向。