Vestergaard Christian L, Génois Mathieu, Barrat Alain
Aix Marseille Université, Université de Toulon, CNRS, CPT, UMR 7332, 13288 Marseille, France.
Aix Marseille Université, Université de Toulon, CNRS, CPT, UMR 7332, 13288 Marseille, France and Data Science Laboratory, ISI Foundation, Torino, Italy.
Phys Rev E Stat Nonlin Soft Matter Phys. 2014 Oct;90(4):042805. doi: 10.1103/PhysRevE.90.042805. Epub 2014 Oct 9.
Empirical temporal networks display strong heterogeneities in their dynamics, which profoundly affect processes taking place on these networks, such as rumor and epidemic spreading. Despite the recent wealth of data on temporal networks, little work has been devoted to the understanding of how such heterogeneities can emerge from microscopic mechanisms at the level of nodes and links. Here we show that long-term memory effects are present in the creation and disappearance of links in empirical networks. We thus consider a simple generative modeling framework for temporal networks able to incorporate these memory mechanisms. This allows us to study separately the role of each of these mechanisms in the emergence of heterogeneous network dynamics. In particular, we show analytically and numerically how heterogeneous distributions of contact durations, of intercontact durations, and of numbers of contacts per link emerge. We also study the individual effect of heterogeneities on dynamical processes, such as the paradigmatic susceptible-infected epidemic spreading model. Our results confirm in particular the crucial role of the distributions of intercontact durations and of the numbers of contacts per link.
实证时间网络在其动态特性方面表现出强烈的异质性，这深刻地影响了在这些网络上发生的过程，如谣言传播和流行病传播。尽管最近有大量关于时间网络的数据，但很少有工作致力于理解这种异质性如何从节点和链接层面的微观机制中产生。在这里，我们表明实证网络中链接的创建和消失存在长期记忆效应。因此，我们考虑一个简单的时间网络生成建模框架，该框架能够纳入这些记忆机制。这使我们能够分别研究这些机制中的每一个在异质网络动态出现中的作用。特别是，我们通过分析和数值方法展示了接触持续时间、接触间隔时间以及每个链接的接触次数的异质分布是如何出现的。我们还研究了异质性对动态过程的个体影响，例如典型的易感 - 感染流行病传播模型。我们的结果特别证实了接触间隔时间分布和每个链接的接触次数分布的关键作用。
