GraphAgent

LLM-based Agents - Generator, Reasonor - LLM4Graph

About The Project

The GraphAgent Framework is a platform built on LLM-based agents, designed to leverage the capabilities of LLMs for various graph data tasks. With its parallel acceleration capabilities, GraphAgent effectively meets the challenges posed by large-scale graph data. Currently, the framework comprises two modules: graph generation and graph reasoning. In the future, we plan to further enhance the GraphAgent framework.
This page is a collection of our work on GraphAgent.

Exploring the Potential of Large Language Models as Predictors in Dynamic Text-Attributed Graphs (Lei et al., 2025)

arxiv

Citation

@misc{lei2025exploring,
      title={Exploring the Potential of Large Language Models as Predictors in Dynamic Text-Attributed Graphs}, 
      author={Runlin Lei and Jiarui Ji and Haipeng Ding and Lu Yi and Zhewei Wei and Yongchao Liu and Chuntao Hong},
      year={2025},
      eprint={2503.03258},
      archivePrefix={arXiv},
      primaryClass={cs.LG},
      url={https://arxiv.org/abs/2503.03258}, 
}

Scalable and Accurate Graph Reasoning with LLM-based Multi-Agents (Hu et al., 2024)

arxiv

Citation

@article{hu2024scalable,
  title={Scalable and Accurate Graph Reasoning with LLM-based Multi-Agents},
  author={Hu, Yuwei and Lei, Runlin and Huang, Xinyi and Wei, Zhewei and Liu, Yongchao},
  journal={arXiv preprint arXiv:2410.05130},
  year={2024}
}

Dynamic and Textual Graph Generation Via Large-Scale LLM-based Agent Simulation (Ji et al., 2025)

arxiv stars stars

Citation

@preprint{ji2025llm,
      title={LLM-Based Multi-Agent Systems are Scalable Graph Generative Models}, 
      author={Jiarui Ji and Runlin Lei and Jialing Bi and Zhewei Wei and Xu Chen and Yankai Lin and Xuchen Pan and Yaliang Li and Bolin Ding},
      year={2025},
      eprint={2410.09824},
      archivePrefix={arXiv},
      primaryClass={cs.CL},
      url={https://arxiv.org/abs/2410.09824}, 
}