Generating social networks is essential for many applications, such as epidemic modeling and social simulations. The emergence of generative AI, especially large language models (LLMs), offers new possibilities for social network generation: LLMs can generate networks without additional training or need to define network parameters, and users can flexibly define individuals in the network using natural language. However, this potential raises two critical questions: 1) are the social networks generated by LLMs realistic, and 2) what are risks of bias, given the importance of demographics in forming social ties? To answer these questions, we develop three prompting methods for network generation and compare the generated networks to a suite of real social networks. We find that more realistic networks are generated with "local" methods, where the LLM constructs relations for one persona at a time, compared to "global" methods that construct the entire network at once. We also find that the generated networks match real networks on many characteristics, including density, clustering, connectivity, and degree distribution. However, we find that LLMs emphasize political homophily over all other types of homophily and significantly overestimate political homophily compared to real social networks.

翻译：生成社交网络对于流行病建模和社会模拟等众多应用至关重要。生成式人工智能尤其是大语言模型的出现为社交网络生成提供了新的可能性：大语言模型无需额外训练或定义网络参数即可生成网络，用户能够通过自然语言灵活定义网络中的个体。然而，这种潜力引发了两个关键问题：1）大语言模型生成的社交网络是否真实；2）鉴于人口统计学特征在形成社会联系中的重要性，存在哪些偏见风险？为回答这些问题，我们开发了三种网络生成的提示方法，并将生成的网络与一系列真实社交网络进行比较。我们发现，相较于"全局"方法（一次性构建整个网络），采用"局部"方法（大语言模型每次为单个人设构建关系）能生成更真实的网络。研究还表明，生成网络在密度、聚类系数、连通性和度分布等诸多特征上与真实网络相符。然而，我们发现大语言模型过度强调政治同质性而忽视其他类型的同质性，且与真实社交网络相比显著高估了政治同质性。