Large language models~(LLM) like ChatGPT have become indispensable to artificial general intelligence~(AGI), demonstrating excellent performance in various natural language processing tasks. In the real world, graph data is ubiquitous and an essential part of AGI and prevails in domains like social network analysis, bioinformatics and recommender systems. The training corpus of large language models often includes some algorithmic components, which allows them to achieve certain effects on some graph data-related problems. However, there is still little research on their performance on a broader range of graph-structured data. In this study, we conduct an extensive investigation to assess the proficiency of LLMs in comprehending graph data, employing a diverse range of structural and semantic-related tasks. Our analysis encompasses 10 distinct tasks that evaluate the LLMs' capabilities in graph understanding. Through our study, we not only uncover the current limitations of language models in comprehending graph structures and performing associated reasoning tasks but also emphasize the necessity for further advancements and novel approaches to enhance their graph processing capabilities. Our findings contribute valuable insights towards bridging the gap between language models and graph understanding, paving the way for more effective graph mining and knowledge extraction.

翻译：大语言模型如ChatGPT已成为通用人工智能不可或缺的部分，在各种自然语言处理任务中展现出卓越性能。现实世界中，图数据无处不在，是通用人工智能的重要组成部分，广泛应用于社交网络分析、生物信息学和推荐系统等领域。大语言模型的训练语料通常包含某些算法组件，使其能在部分图数据相关问题上取得一定效果。然而，关于它们在更广泛图结构化数据上的表现仍缺乏研究。本研究通过一系列结构性和语义相关任务，全面评估了大语言模型理解图数据的能力。我们的分析涵盖10个不同任务，用以评估大语言模型的图理解能力。通过研究，我们不仅揭示了当前语言模型在图结构理解及相关推理任务中的局限性，还强调了需要进一步创新和开发新方法以提升其图处理能力。我们的发现为弥合语言模型与图理解之间的鸿沟提供了宝贵见解，为更高效的图挖掘和知识提取铺平了道路。