Foundation models have emerged as critical components in a variety of artificial intelligence applications, and showcase significant success in natural language processing and several other domains. Meanwhile, the field of graph machine learning is witnessing a paradigm transition from shallow methods to more sophisticated deep learning approaches. The capabilities of foundation models to generalize and adapt motivate graph machine learning researchers to discuss the potential of developing a new graph learning paradigm. This paradigm envisions models that are pre-trained on extensive graph data and can be adapted for various graph tasks. Despite this burgeoning interest, there is a noticeable lack of clear definitions and systematic analyses pertaining to this new domain. To this end, this article introduces the concept of Graph Foundation Models (GFMs), and offers an exhaustive explanation of their key characteristics and underlying technologies. We proceed to classify the existing work related to GFMs into three distinct categories, based on their dependence on graph neural networks and large language models. In addition to providing a thorough review of the current state of GFMs, this article also outlooks potential avenues for future research in this rapidly evolving domain.

翻译：基础模型已成为各种人工智能应用中的关键组成部分，并在自然语言处理及其他多个领域展现出显著成功。与此同时，图机器学习领域正经历着从浅层方法向更复杂深度学习范式的转变。基础模型所具备的泛化与适应能力，促使图机器学习研究者开始探讨发展新型图学习范式的可能性。该范式设想模型能够在海量图数据上进行预训练，并能适配多种图任务。尽管相关兴趣日益增长，但这一新兴领域目前仍缺乏清晰的定义与系统化的分析。为此，本文提出了图基础模型（Graph Foundation Models, GFMs）的概念，并对其关键特征与支撑技术进行了详尽阐释。我们依据现有工作对图神经网络及大语言模型的依赖程度，将其划分为三个类别。除对GFMs现状进行全面梳理外，本文亦对这一快速发展领域的未来研究方向进行了展望。