Improving the general capabilities of large language models (LLMs) is an active research topic. As a common data structure in many real-world domains, understanding graph data is a crucial part of advancing general intelligence. To this end, we propose a dynamic benchmark named GraphInstruct in this paper, which comprehensively includes 21 classical graph reasoning tasks, providing diverse graph generation pipelines and detailed intermediate reasoning steps for each sample. Based on GraphInstruct, we develop GraphSolver via efficient instruction-tuning, which demonstrates prominent graph understanding capability compared to other open-sourced LLMs. To further endow LLMs with multi-step graph reasoning capability, we propose a label-mask training strategy and build GraphSolver+, which leverages masked supervision on intermediate reasoning tokens to emphasize crucial node-identification signals. As one of the pioneering efforts to enhance the graph understanding and reasoning abilities of LLMs, extensive experiments have demonstrated the superiority of GraphSolver and GraphSolver+ over other LLMs. We sincerely hope GraphInstruct will facilitate further research on applying LLMs to graph-structured data. Our code and data are released publicly at: https://github.com/CGCL-codes/GraphInstruct.

翻译：提升大语言模型的通用能力是当前活跃的研究方向。图作为一种在众多现实领域常见的数据结构，理解图数据是推动通用智能发展的关键环节。为此，本文提出一个名为GraphInstruct的动态基准，全面涵盖了21项经典图推理任务，为每个样本提供了多样化的图生成流程和详细的中间推理步骤。基于GraphInstruct，我们通过高效的指令微调开发了GraphSolver，相比其他开源大语言模型，其展现出卓越的图理解能力。为进一步赋予大语言模型多步图推理能力，我们提出了一种标签掩码训练策略，并构建了GraphSolver+，该模型利用对中间推理标记的掩码监督来强化关键节点识别信号。作为增强大语言模型图理解与推理能力的开创性尝试之一，大量实验证明了GraphSolver和GraphSolver+相较于其他大语言模型的优越性。我们诚挚希望GraphInstruct能促进大语言模型在图结构数据应用上的进一步研究。我们的代码与数据已公开于：https://github.com/CGCL-codes/GraphInstruct。