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：像ChatGPT这样的大型语言模型（LLM）已成为通用人工智能（AGI）不可或缺的一部分，在各类自然语言处理任务中表现出色。在现实世界中，图数据无处不在，是AGI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。大型语言模型的训练语料库通常包含一些算法组件，使其能够在某些图数据相关问题上取得一定效果。然而，关于它们在更广泛的图结构数据上的性能，相关研究仍然较少。本研究通过一系列多样化的结构性和语义相关任务，对LLMs理解图数据的能力进行了广泛调查。我们的分析涵盖了10个不同任务，用以评估LLMs在图理解方面的能力。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应推理任务上的局限性，还强调了需要进一步推进和采用新方法来提升其图处理能力。我们的发现为弥合语言模型与图理解之间的差距提供了宝贵见解，为更有效的图挖掘和知识提取铺平了道路。