GraphAgent

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.

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

1. Scalable and Accurate Graph Reasoning with LLM-based Multi-Agents
   Yuwei Hu, Runlin Lei, Xinyi Huang, Zhewei Wei, and Yongchao Liu
   arXiv, 2024

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., 2024)

1. Dynamic and Textual Graph Generation Via Large-Scale LLM-based Agent Simulation
   Jiarui Ji, Runlin Lei, Jialing Bi, Zhewei Wei, Yankai Lin, Xuchen Pan, Yaliang Li, and Bolin Ding
   arXiv, 2024

Citation

@article{ji2024dynamic,
  title={Dynamic and Textual Graph Generation Via Large-Scale LLM-based Agent Simulation},
  author={Ji, Jiarui and Lei, Runlin and Bi, Jialing and Wei, Zhewei and Lin, Yankai and Pan, Xuchen and Li, Yaliang and Ding, Bolin},
  journal={arXiv preprint arXiv:2410.09824},
  year={2024}
}