Network Formation and Dynamics Among Multi-LLMs

• URL: http://arxiv.org/abs/2402.10659v4
• Date: Thu, 05 Dec 2024 04:35:22 GMT
• Title: Network Formation and Dynamics Among Multi-LLMs
• Authors: Marios Papachristou, Yuan Yuan,
• Abstract summary: Large language models (LLMs) like GPT, Claude, and Llama increasingly integrate into social and professional settings.<n>This study develops a framework to examine whether the network formation behaviors of multiple LLMs approximate certain aspects of human network dynamics.
• Score: 5.8418144988203915
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Social networks fundamentally shape human opinions, behaviors, and the dissemination of information. As large language models (LLMs) like GPT, Claude, and Llama increasingly integrate into social and professional settings, understanding their behavior in the context of social interactions and network formation becomes essential. This study develops a framework to systematically examine whether the network formation behaviors of multiple LLMs approximate certain aspects of human network dynamics. By simulating interactions among LLM agents across various model families, we observe that these models consistently exhibit key patterns associated with social network principles including preferential attachment, triadic closure, homophily, community structure, and the small-world phenomenon when forming networks. Moreover, LLMs adapt their network formation strategies based on each network's characteristics, reflecting the context-dependent nature of human behavior: in Facebook networks, they prioritize triadic closure and homophily, mirroring close-knit friendships; in phone networks, homophily and preferential attachment dominate, capturing personal and professional connections, while in employment networks, LLMs favor heterophily and high-degree connections, aligning with career advancement dynamics. These results open new avenues for using LLMs in network science research, with potential applications in agent-based modeling and synthetic network generation.

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