异质社会资本配置网络动态的渐近理论
Asymptotic theory for the dynamic of networks with heterogenous social capital allocation
摘要：
社会网络的结构和动态在很大程度上取决于个体的异质互动活动和社会资本配置。这些特征在网络的形成过程中以一种非平凡的方式相互作用，并对严格的网络演化动力系统理论提出了挑战。在这里，我们研究了七个真实的网络，描述了三种不同环境下的时间人类互动：科学合作，Twitter提及和手机通话。我们发现，节点的活动和社会资本分配可以描述为两个一般的函数形式，可以用来定义一个简单的随机模型的社会网络动态。该模型允许描述系统动态的主方程的显式渐近解，并提供了表征社会网络度分布和个体节点自我网络的时间演化的标度律。分析预测准确地再现了验证理论方法的经验观察。我们的研究结果提供了一个严格的动力系统框架，可以扩展到包括网络的形成的其他功能，并产生数据驱动的预测大规模社交网络的渐近行为。
英文摘要：
The structure and dynamic of social network are largely determined by the heterogeneous interaction activity and social capital allocation of individuals. These features interplay in a non-trivial way in the formation of network and challenge a rigorous dynamical system theory of network evolution. Here we study seven real networks describing temporal human interactions in three different settings: scientific collaborations, Twitter mentions, and mobile phone calls. We find that the node's activity and social capital allocation can be described by two general functional forms that can be used to define a simple stochastic model for social network dynamic. This model allows the explicit asymptotic solution of the Master Equation describing the system dynamic, and provides the scaling laws characterizing the time evolution of the social network degree distribution and individual node's ego network. The analytical predictions reproduce with accuracy the empirical observations validating the theoretical approach. Our results provide a rigorous dynamical system framework that can be extended to include other features of networks' formation and to generate data driven predictions for the asymptotic behavior of large-scale social networks.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
