Beyond Individual Mimicry: Constructing human-like social network with Graph-augmented LLM agents

ACL ARR 2026 January Submission2194 Authors

Keywords: LLM, Social bot, Multi-agent, simulation

Abstract:

Driven by large language models (LLMs), a new generation of social bots can autonomously interact locally, whose human-like behaviors enable them to evade social bot detection.
However, while these botnets exhibit realistic local social interactions, they fail to preserve human-like social network.
This is because LLM-based bots are graph-unaware and cannot coordinate over global interactions, which makes those botnets vulnerable to graph neural network (GNN)-based detection.
To address this limitation, we propose GraphMind, which equips LLM-driven social bots to explicitly learn and fit human-like social network structures. Building on this foundation, we further construct GraphMind-Botnet, a LLM-driven botnet designed to evaluate the performance of existing social bot detection algorithms.
Experiments on datasets derived from GraphMind-Botnet show that both text-based and graph-based detection models struggle to distinguish human users from bots. Our results highlight the critical role of social link construction in LLM-driven social network generation, while exposing fundamental weaknesses in existing bot detection mechanisms.

Paper Type: Long

Research Area: AI/LLM Agents

Research Area Keywords: NLP Applications,Resources and Evaluation

Contribution Types: NLP engineering experiment, Publicly available software and/or pre-trained models

Languages Studied: English

Submission Number: 2194