GraphInstruct: Empowering Large Language Models with Graph Understanding and Reasoning Capability
National Engineering Research Center for Big Data Technology and System（大数据技术与系统国家工程研究中心）；System Lab, Cluster（系统实验室，集群）；Grid Computing Lab, School of Computer Science（网格计算实验室，计算机科学学院）；Huazhong University of Science and Technology（华中科技大学）；School of Computer Science（计算机科学学院）；Microsoft Research Asia, Beijing 100190, China（微软亚洲研究院，北京100190，中国）
Zihan Luo, Xiran Song, Hong Huang, Jianxun Lian, Chenhao Zhang, Jinqi Jiang, Xing Xie, Hai Jin
备注：
The article has been accepted by Frontiers of Computer Science (FCS), with the DOI: {10.1007/s11704-025-51382-0}
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.
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