@inproceedings{ji-etal-2025-llm,
    title = "{LLM}-Based Multi-Agent Systems are Scalable Graph Generative Models",
    author = "Ji, Jiarui  and
      Lei, Runlin  and
      Bi, Jialing  and
      Wei, Zhewei  and
      Chen, Xu  and
      Lin, Yankai  and
      Pan, Xuchen  and
      Li, Yaliang  and
      Ding, Bolin",
    editor = "Che, Wanxiang  and
      Nabende, Joyce  and
      Shutova, Ekaterina  and
      Pilehvar, Mohammad Taher",
    booktitle = "Findings of the Association for Computational Linguistics: ACL 2025",
    month = jul,
    year = "2025",
    address = "Vienna, Austria",
    publisher = "Association for Computational Linguistics",
    url = "https://preview.aclanthology.org/ingestion-acl-25/2025.findings-acl.78/",
    pages = "1492--1523",
    ISBN = "979-8-89176-256-5",
    abstract = "The structural properties of naturally arising social graphs are extensively studied to understand their evolution. Prior approaches for modeling network dynamics typically rely on rule-based models, which lack realism and generalizability, or deep learning-based models, which require large-scale training datasets. As abstract graph representations of entity-wise interactions, social graphs present an opportunity to explore network evolution mechanisms through realistic simulations of human-item interactions. Leveraging the pre-trained social consensus knowledge embedded in large language models (LLMs), we present GraphAgent-Generator (GAG), a novel simulation-based framework for dynamic, text-attributed social graph generation. GAG simulates the temporal node and edge generation processes for zero-shot social graph generation. The resulting graphs adhere to seven key macroscopic network properties, achieving an 11{\%} improvement in microscopic graph structure metrics. Through the node classification benchmarking task, we validate that GAG effectively captures the intricate text-structure correlations in graph generation. Furthermore, GAG supports generating graphs with up to nearly 100,000 nodes or 10 million edges through large-scale LLM-based agent simulation with parallel acceleration, achieving a minimum speed-up of 90.4{\%}. The source code is available at https://github.com/Ji-Cather/GraphAgent."
}