Large language models (LLMs) such as GPT-4 have emerged as frontrunners, showcasing unparalleled prowess in diverse applications, including answering queries, code generation, and more. Parallelly, graph-structured data, an intrinsic data type, is pervasive in real-world scenarios. Merging the capabilities of LLMs with graph-structured data has been a topic of keen interest. This paper bifurcates such integrations into two predominant categories. The first leverages LLMs for graph learning, where LLMs can not only augment existing graph algorithms but also stand as prediction models for various graph tasks. Conversely, the second category underscores the pivotal role of graphs in advancing LLMs. Mirroring human cognition, we solve complex tasks by adopting graphs in either reasoning or collaboration. Integrating with such structures can significantly boost the performance of LLMs in various complicated tasks. We also discuss and propose open questions for integrating LLMs with graph-structured data for the future direction of the field.

翻译：大语言模型（如GPT-4）已成为前沿技术，在查询回答、代码生成等多种应用中展现出无与伦比的能力。与此同时，图结构数据作为一种内在的数据类型，在现实场景中普遍存在。将大语言模型的能力与图结构数据相融合一直是备受关注的研究课题。本文将此类融合分为两大主要类别：第一类是利用大语言模型进行图学习，大语言模型不仅能增强现有图算法，还可作为各类图任务的预测模型；第二类则强调图在推动大语言模型发展中的关键作用。通过模拟人类认知，我们可在推理或协作过程中借助图来求解复杂任务，这种与图结构的融合能够显著提升大语言模型在多种复杂任务中的性能。本文还探讨并提出了该领域未来方向中融合大语言模型与图结构数据的开放性问题。