Zhao Kun, Stehlé Juliette, Bianconi Ginestra, Barrat Alain
Department of Physics, Northeastern University, Boston, Massachusetts 02115, USA.
Phys Rev E Stat Nonlin Soft Matter Phys. 2011 May;83(5 Pt 2):056109. doi: 10.1103/PhysRevE.83.056109. Epub 2011 May 12.
The recent availability of data describing social networks is changing our understanding of the "microscopic structure" of a social tie. A social tie indeed is an aggregated outcome of many social interactions such as face-to-face conversations or phone calls. Analysis of data on face-to-face interactions shows that such events, as many other human activities, are bursty, with very heterogeneous durations. In this paper we present a model for social interactions at short time scales, aimed at describing contexts such as conference venues in which individuals interact in small groups. We present a detailed analytical and numerical study of the model's dynamical properties, and show that it reproduces important features of empirical data. The model allows for many generalizations toward an increasingly realistic description of social interactions. In particular, in this paper we investigate the case where the agents have intrinsic heterogeneities in their social behavior, or where dynamic variations of the local number of individuals are included. Finally we propose this model as a very flexible framework to investigate how dynamical processes unfold in social networks.
最近有关社交网络的数据可用性正在改变我们对社会关系“微观结构”的理解。社会关系确实是许多社交互动（如面对面交谈或电话通话）的综合结果。对面对面互动数据的分析表明，此类事件与许多其他人类活动一样，具有突发性，持续时间差异很大。在本文中，我们提出了一个短时间尺度下的社交互动模型，旨在描述诸如会议场所等个体在小群体中互动的情境。我们对该模型的动力学特性进行了详细的分析和数值研究，并表明它再现了经验数据的重要特征。该模型可以进行许多推广，以更逼真地描述社交互动。特别是，在本文中我们研究了个体在社交行为中具有内在异质性，或包含局部个体数量动态变化的情况。最后，我们将此模型作为一个非常灵活的框架，用于研究动态过程如何在社交网络中展开。
