Social networks influence behaviors, preferences, and relationships and play a crucial role in the dissemination of information and norms within human societies. As large language models (LLMs) increasingly integrate into social and professional environments, understanding their behavior within the context of social networks and interactions becomes essential. Our study analyzes the behaviors of standard network structures and real-world networks to determine whether the dynamics of multiple LLMs align with human social dynamics. We explore various social network principles, including micro-level concepts such as preferential attachment, triadic closure, and homophily, as well as macro-level concepts like community structure and the small-world phenomenon. Our findings suggest that LLMs demonstrate all these principles when they are provided with network structures and asked about their preferences regarding network formation. Furthermore, we investigate LLMs' decision-making based on real-world networks to compare the strengths of these principles. Our results reveal that triadic closure and homophily have a stronger influence than preferential attachment and that LLMs substantially exceed random guessing in the task of network formation predictions. Overall, our study contributes to the development of socially aware LLMs by shedding light on LLMs' network formation behaviors and exploring their impacts on social dynamics and norms.

翻译：社交网络影响人类的行为、偏好及人际关系，在信息与规范的传播中发挥关键作用。随着大型语言模型（LLM）日益融入社会与专业环境，理解其在社交网络与交互背景下的行为变得至关重要。本研究分析了标准网络结构与真实网络中的行为模式，以探究多LLM动态是否与人类社会动态一致。我们探索了包括微观层面（如优先连接、三元闭包和同质性）以及宏观层面（如社区结构和小世界现象）的多种社交网络原理。研究结果表明，当LLM被赋予网络结构并被问及网络形成偏好时，它们能够展现出所有这些原理。此外，我们基于真实网络考察LLM的决策过程，以比较这些原理的强度差异。研究结果显示，三元闭包与同质性的影响显著强于优先连接，且LLM在网络形成预测任务中的表现远超随机猜测。总体而言，本研究通过揭示LLM的网络形成行为并探索其对社交动态与规范的影响，为发展具有社会意识的LLM做出了贡献。