Graph-structured data are the commonly used and have wide application scenarios in the real world. For these diverse applications, the vast variety of learning tasks, graph domains, and complex graph learning procedures present challenges for human experts when designing versatile graph learning approaches. Facing these challenges, large language models (LLMs) offer a potential solution due to the extensive knowledge and the human-like intelligence. This paper proposes a novel conceptual prototype for designing versatile graph learning methods with LLMs, with a particular focus on the ``where'' and ``how'' perspectives. From the ``where'' perspective, we summarize four key graph learning procedures, including task definition, graph data feature engineering, model selection and optimization, deployment and serving. We then explore the application scenarios of LLMs in these procedures across a wider spectrum. In the ``how'' perspective, we align the abilities of LLMs with the requirements of each procedure. Finally, we point out the promising directions that could better leverage the strength of LLMs towards versatile graph learning methods.

本文提出了一种使用大型语言模型设计多功能图学习方法的新概念原型，重点关注了“在哪里”和“如何”的角度，总结了四个关键的图学习过程，并探索了大型语言模型在这些过程中的应用场景，最后指出了更好利用大型语言模型的发展方向。

通向多功能图学习方法：基于大型语言模型的视角