论文标题
社会群体的普遍增长：经验分析和建模
Universal growth of social groups: empirical analysis and modeling
论文作者
论文摘要
社会群体是任何社会制度的基本要素。它们的出现和进化与社会体系的结构和动态密切相关。社会群体的研究主要集中在社会系统成员的相互作用网络的增长和结构上，以及成员群体的隶属关系如何影响这些网络的发展。尚未详细研究小组规模的分布以及成员如何加入组。在这里，我们结合了统计物理和复杂的网络理论工具，以分析三个数据集中的组大小分布，即伦敦，纽约和雷迪特的聚会组。我们表明，这三个分布均表现出对数正态行为，表明这些系统中的普遍增长模式。我们提出了一个理论模型，该模型结合了群体之间成员的社会和随机扩散，以模拟社会互动的作用以及成员对社会群体增长的兴趣。模拟结果表明，我们的模型再现了在经验数据中观察到的生长模式。此外，我们的分析表明，社交互动对于在线群体（例如Reddit）的扩散而不是在线群体（例如聚会）中更为重要。这项工作表明，社会群体遵循普遍的增长机制，在建模社会系统的发展时需要考虑。
Social groups are fundamental elements of any social system. Their emergence and evolution are closely related to the structure and dynamics of a social system. Research on social groups was primarily focused on the growth and the structure of the interaction networks of social system members and how members' group affiliation influences the evolution of these networks. The distribution of groups' size and how members join groups has not been investigated in detail. Here we combine statistical physics and complex network theory tools to analyze the distribution of group sizes in three data sets, Meetup groups based in London and New York and Reddit. We show that all three distributions exhibit log-normal behavior that indicates universal growth patterns in these systems. We propose a theoretical model that combines social and random diffusion of members between groups to simulate the roles of social interactions and members' interest in the growth of social groups. The simulation results show that our model reproduces growth patterns observed in empirical data. Moreover, our analysis shows that social interactions are more critical for the diffusion of members in online groups, such as Reddit, than in offline groups, such as Meetup. This work shows that social groups follow universal growth mechanisms that need to be considered in modeling the evolution of social systems.
