@inproceedings{ji-etal-2026-graphia,
    title = "{GRAPHIA}: Harnessing Social Graph Data to Enhance {LLM}-Based Social Simulation",
    author = "Ji, Jiarui  and
      Zhang, Zehua  and
      Wei, Zhewei  and
      Tong, Bin  and
      Wang, Guan  and
      Zheng, Bo",
    editor = "Liakata, Maria  and
      Moreira, Viviane P.  and
      Zhang, Jiajun  and
      Jurgens, David",
    booktitle = "Proceedings of the 64th Annual Meeting of the {A}ssociation for {C}omputational {L}inguistics (Volume 1: Long Papers)",
    month = jul,
    year = "2026",
    address = "San Diego, California, United States",
    publisher = "Association for Computational Linguistics",
    url = "https://preview.aclanthology.org/check-for-anonymous-pdfs/2026.acl-long.322/",
    pages = "7103--7128",
    ISBN = "979-8-89176-390-6",
    abstract = "Large language models (LLMs) have shown promise in simulating human-like social behaviors. Social graphs provide high-quality supervision signals that encode both local interactions and global network structure, yet they remain underutilized for LLM training. To address this gap, we propose Graphia, the first general LLM-based social graph simulation framework that leverages graph data as supervision for LLM post-training via reinforcement learning. With GNN-based structural rewards, Graphia trains specialized agents to predict whom to interact with (destination selection) and how to interact (edge generation), followed by designed graph generation pipelines. We evaluate Graphia under two settings: Transductive Dynamic Graph Generation (TDGG), a micro-level task with our proposed node-wise interaction alignment metrics; and Inductive Dynamic Graph Generation (IDGG), a macro-level task with our proposed metrics for aligning emergent network properties. On three real-world networks, Graphia improves micro-level alignment by 6.1{\%} in the composite destination selection score, 12{\%} in edge classification accuracy, and 27.9{\%} in edge content BERTScore over the strongest baseline. For macro-level alignment, it achieves 35.98{\%} higher structural similarity and 28.71{\%} better replication of social phenomena such as power laws and echo chambers. Our results show that social graphs can serve as high-quality supervision signals for LLM post-training, closing the gap between agent behaviors and network dynamics for LLM-based simulation. Code is available at https://github.com/Ji-Cather/Graphia.git."
}