Graphs are an essential data structure utilized to represent relationships in real-world scenarios. Prior research has established that Graph Neural Networks (GNNs) deliver impressive outcomes in graph-centric tasks, such as link prediction and node classification. Despite these advancements, challenges like data sparsity and limited generalization capabilities continue to persist. Recently, Large Language Models (LLMs) have gained attention in natural language processing. They excel in language comprehension and summarization. Integrating LLMs with graph learning techniques has attracted interest as a way to enhance performance in graph learning tasks. In this survey, we conduct an in-depth review of the latest state-of-the-art LLMs applied in graph learning and introduce a novel taxonomy to categorize existing methods based on their framework design. We detail four unique designs: i) GNNs as Prefix, ii) LLMs as Prefix, iii) LLMs-Graphs Integration, and iv) LLMs-Only, highlighting key methodologies within each category. We explore the strengths and limitations of each framework, and emphasize potential avenues for future research, including overcoming current integration challenges between LLMs and graph learning techniques, and venturing into new application areas. This survey aims to serve as a valuable resource for researchers and practitioners eager to leverage large language models in graph learning, and to inspire continued progress in this dynamic field. We consistently maintain the related open-source materials at \url{https://github.com/HKUDS/Awesome-LLM4Graph-Papers}.

翻译：图是一种用于表示现实世界关系的基本数据结构。先前的研究表明，图神经网络（GNNs）在图中心任务（如链接预测和节点分类）中取得了显著成果。尽管取得了这些进展，但数据稀疏性和泛化能力有限等挑战依然存在。近年来，大语言模型（LLMs）在自然语言处理领域备受关注，其在语言理解和摘要生成方面表现出色。将LLMs与图学习技术相结合，作为提升图学习任务性能的一种途径，已引起研究兴趣。本综述深入回顾了应用于图学习的最新前沿LLMs，并提出了一种新颖的分类法，以根据其框架设计对现有方法进行分类。我们详细阐述了四种独特的设计：i) GNN作为前缀，ii) LLM作为前缀，iii) LLM-图集成，以及iv) 纯LLM，并重点介绍了每个类别中的关键方法。我们探讨了每种框架的优势与局限性，并强调了未来研究的潜在方向，包括克服当前LLMs与图学习技术集成中的挑战，以及探索新的应用领域。本综述旨在为渴望在图学习中利用大语言模型的研究人员和从业者提供有价值的资源，并激励这一动态领域的持续进展。我们持续维护相关的开源材料于 \url{https://github.com/HKUDS/Awesome-LLM4Graph-Papers}。