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Large language models~(LLM) like ChatGPT have become indispensable toartificial general intelligence~(AGI), demonstrating excellent performance invarious natural language processing tasks. In the real world, graph data isubiquitous and an essential part of AGI and prevails in domains like socialnetwork analysis, bioinformatics and recommender systems. The training corpusof large language models often includes some algorithmic components, whichallows them to achieve certain effects on some graph data-related problems.However, there is still little research on their performance on a broader rangeof graph-structured data. In this study, we conduct an extensive investigationto assess the proficiency of LLMs in comprehending graph data, employing adiverse range of structural and semantic-related tasks. Our analysisencompasses 10 distinct tasks that evaluate the LLMs' capabilities in graphunderstanding. Through our study, we not only uncover the current limitationsof language models in comprehending graph structures and performing associatedreasoning tasks but also emphasize the necessity for further advancements andnovel approaches to enhance their graph processing capabilities. Our findingscontribute valuable insights towards bridging the gap between language modelsand graph understanding, paving the way for more effective graph mining andknowledge extraction.

GPT4Graph: Can Large Language Models Understand Graph Structured Data ? An Empirical Evaluation and Benchmarking