mlsanityData to 14 Oct 2026

Emergence of Scale-Free Networks in Social Interactions among Large Language Models

arXiv 2312.06619 ↗First posted 11 Dec 2023physics.soc-ph cs.CYcited by 20

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Abstract (arXiv)

Scale-free networks are one of the most famous examples of emergent behavior and are ubiquitous in social systems, especially online social media in which users can follow each other. By analyzing the interactions of multiple generative agents using GPT3.5-turbo as a language model, we demonstrate their ability to not only mimic individual human linguistic behavior but also exhibit collective phenomena intrinsic to human societies, in particular the emergence of scale-free networks. We discovered that this process is disrupted by a skewed token prior distribution of GPT3.5-turbo, which can lead to networks with extreme centralization as a kind of alignment. We show how renaming agents removes these token priors and allows the model to generate a range of networks from random networks to more realistic scale-free networks.

Key facts. Cited by 20 papers in the map (data through 14 Oct 2026). First posted on arXiv 11 Dec 2023.

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Citing papers per year: 2024: 5; 2025: 7; 2026: 8.

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  1. 2024Emergent social conventions and collective bias in LLM populationscited by 123
  2. 2024Y Social: an LLM-powered Social Media Digital Twincited by 44
  3. 2024Network Formation and Dynamics Among Multi-LLMscited by 29
  4. 2025Do Large Language Models Solve the Problems of Agent-Based Modeling? A Critical Review of Generative Social Simulationscited by 28
  5. 2025Emergence of polarization in networks of large language model agentscited by 26
  6. 2026Conformity and Social Impact on AI Agentscited by 16
  7. 2025The PIMMUR Principles: Ensuring Validity in Collective Behavior of LLM Societiescited by 14
  8. 2024Evolving AI Collectives to Enhance Human Diversity and Enable Self-Regulationcited by 13
  9. 2026Collective Behavior of AI Agents: the Case of Moltbookcited by 13
  10. 2024Social Opinions Prediction Utilizes Fusing Dynamics Equation with LLM-based Agentscited by 11
  11. 2025Computational Turing Test Reveals Systematic Differences Between Human and AI Languagecited by 6
  12. 2026Generative AI collective behavior needs an interactionist paradigmcited by 6
8 more
  1. 2026Gender Dynamics and Homophily in a Social Network of LLM Agentscited by 5
  2. 2025Disentangling Interaction and Bias Effects in Opinion Dynamics of Large Language Modelscited by 2
  3. 2025The Collective Turing Test: Large Language Models Can Generate Realistic Multi-User Discussionscited by 2
  4. 2026Agentic Microphysics: A Manifesto for Generative AI Safetycited by 2
  5. 2025How Large Language Models play humans in online conversations: a simulated study of the 2016 US politics on Redditcited by 0
  6. 2026Collective Opinion Dynamics in Structured LLM Populationscited by 0
  7. 2026LLMs for Social Network Modeling: From Network Generation to Dynamic Processescited by 0
  8. 2026Illusory Truth or Mere Exposure? Model-Dependent Repetition Effects in LLM-Based Social Media Simulationscited by 0

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  1. 2026Emergence of Fragility in LLM-based Social Networks: the Case of Moltbook
  2. 2024Scaling Large Language Model-based Multi-Agent Collaboration
  3. 2023S3: Social-network Simulation System with Large Language Model-Empowered Agents
  4. 2025Don't Trust Generative Agents to Mimic Communication on Social Networks Unless You Benchmarked their Empirical Realism
  5. 2026Attributing Emergence in Million-Agent Systems
  6. 2024Generative Emergent Communication: Large Language Model is a Collective World Model
  7. 2024Large Language Models can Achieve Social Balance
  8. 2026Predicting the scale limits of social mechanisms in agent societies