Large Language Models (LLMs) have garnered considerable interest within both academic and industrial. Yet, the application of LLMs to graph data remains under-explored. In this study, we evaluate the capabilities of four LLMs in addressing several analytical problems with graph data. We employ four distinct evaluation metrics: Comprehension, Correctness, Fidelity, and Rectification. Our results show that: 1) LLMs effectively comprehend graph data in natural language and reason with graph topology. 2) GPT models can generate logical and coherent results, outperforming alternatives in correctness. 3) All examined LLMs face challenges in structural reasoning, with techniques like zero-shot chain-of-thought and few-shot prompting showing diminished efficacy. 4) GPT models often produce erroneous answers in multi-answer tasks, raising concerns in fidelity. 5) GPT models exhibit elevated confidence in their outputs, potentially hindering their rectification capacities. Notably, GPT-4 has demonstrated the capacity to rectify responses from GPT-3.5-turbo and its own previous iterations. The code is available at: https://github.com/Ayame1006/LLMtoGraph.

翻译：大语言模型（LLMs）在学术界和工业界引起了广泛关注。然而，LLMs在图数据上的应用仍探索不足。本研究评估了四种LLMs在解决图数据若干分析问题中的能力。我们采用四种不同的评估指标：理解力、正确性、忠实度和校正能力。结果表明：1）LLMs能有效理解以自然语言描述的图数据，并基于图拓扑进行推理。2）GPT模型能生成逻辑连贯的结果，在正确性上优于其他模型。3）所有被评估的LLMs在结构推理中均面临挑战，零样本思维链和少样本提示等技术效果减弱。4）GPT模型在多答案任务中常产生错误答案，引发对其忠实度的担忧。5）GPT模型对自身输出表现出较高置信度，可能阻碍其校正能力。值得注意的是，GPT-4展现了校正GPT-3.5-turbo及其自身先前迭代响应的能力。代码见：https://github.com/Ayame1006/LLMtoGraph。