Evaluating and enhancing the general capabilities of large language models
(LLMs) has been an important research topic. Graph is a common data structure
in the real world, and understanding graph data is a crucial part for advancing
general intelligence. To evaluate and enhance the graph understanding abilities
of LLMs, in this paper, we propose a benchmark named GraphInstruct, which
comprehensively includes 21 classical graph reasoning tasks, providing diverse
graph generation pipelines and detailed reasoning steps. Based on
GraphInstruct, we further construct GraphLM through efficient
instruction-tuning, which shows prominent graph understanding capability. In
order to enhance the LLM with graph reasoning capability as well, we propose a
step mask training strategy, and construct a model named GraphLM+. As one of
the pioneering efforts to enhance the graph understanding and reasoning
abilities of LLMs, extensive experiments have demonstrated the superiority of
GraphLM and GraphLM+ over other LLMs. We look forward to more researchers
exploring the potential of LLMs in the graph data mining domain through
GraphInstruct. Our code for generating GraphInstruct is released publicly at:
this https URL

通过提出一个名为 GraphInstruct 的基准测试以及 GraphLM 和 GraphLM + 模型的构建和实验，本文旨在评估和增强大型语言模型在图形理解和推理方面的能力。