Social networks shape opinions, behaviors, and information dissemination in human societies. As large language models (LLMs) increasingly integrate into social and professional environments, understanding their behavior within the context of social interactions and networks becomes essential. Our study analyzes LLMs' network formation behavior to examine whether the dynamics of multiple LLMs are similar to or different from human social dynamics. We observe that LLMs exhibit key social network principles, including preferential attachment, triadic closure, homophily, community structure, and the small-world phenomenon, when asked about their preferences in network formation. We also investigate LLMs' decision-making based on real-world networks, revealing that triadic closure and homophily have a stronger influence than preferential attachment and that LLMs perform well in network formation predictions. Overall, our study opens up new possibilities for using LLMs in network science research and helps develop socially aware LLMs by shedding light on their network formation behaviors and exploring their impacts on social dynamics.

翻译：社交网络塑造着人类社会中的观点、行为与信息传播。随着大语言模型（LLMs）日益融入社会与专业环境，理解其在社交互动与网络背景下的行为变得至关重要。本研究通过分析LLMs的网络形成行为，探究多LLM系统的动态模式与人类社会动态的异同。研究发现，当被问及网络形成偏好时，LLMs展现出关键的社会网络原理，包括优先连接、三元闭包、同质性、社区结构以及小世界现象。我们还基于真实世界网络考察了LLMs的决策机制，发现三元闭包与同质性对决策的影响强于优先连接，且LLMs在网络形成预测中表现优异。总体而言，本研究表明LLMs在网络科学研究中的应用潜力，并通过揭示其网络形成行为及对社会动态的影响，为发展具备社会意识的LLMs提供新思路。