Structure of triadic relations in multiplex networks
Structure of triadic relations in multiplex networks
复制标题
DOI：
10.1088/1367-2630/17/7/073029
复制
发表时间：
2015-07-31
影响因子：
3.3
通讯作者：
Moreno, Yamir
中科院分区：
文献类型：
--
作者：
Cozzo, Emanuele;Kivelae, Mikko;Moreno, Yamir
网络系统研究的最新进展强调，我们的相互联系的世界由通过不同的“层”相互耦合的网络组成，每个网络都代表了许多可能的子系统或相互作用类型之一。但是，传统上将多层网络汇总到单个加权网络中以利用现有工具。诚然，这很方便，但这也是非常有问题的，因为结果可能会丢失重要信息。因此，重要的是要开发网络概念的多层概括。在本文中，我们分析了三合会关系，并概括了对多路复用网络的传播概念。通过专注于产生最简单类型的传递性类型的三元关系，我们将聚类系数的概念和计算推广到多重网络。我们展示了此类网络的分层结构如何引入一种新的自由度，该自由度对传递性具有根本影响。我们计算几个真实多重网络的多路复用聚类系数，并说明为什么在将标准网络概念概括到多重网络时必须非常小心。我们还为随机多重网络家族中网络的集合平均值的聚类系数得出分析表达式。我们的分析表明，社交网络具有强烈的趋势，可以通过在每一层关闭三合会来促进冗余，从而与运输网络具有不同类型的多重传递性，而运输网络没有表现出如此趋势。如果仅研究聚合网络，这些见解是看不见的。
Recent advances in the study of networked systems have highlighted that our interconnected world is composed of networks that are coupled to each other through different 'layers' that each represent one of many possible subsystems or types of interactions. Nevertheless, it is traditional to aggregate multilayer networks into a single weighted network in order to take advantage of existing tools. This is admittedly convenient, but it is also extremely problematic, as important information can be lost as a result. It is therefore important to develop multilayer generalizations of network concepts. In this paper, we analyze triadic relations and generalize the idea of transitivity to multiplex networks. By focusing on triadic relations, which yield the simplest type of transitivity, we generalize the concept and computation of clustering coefficients to multiplex networks. We show how the layered structure of such networks introduces a new degree of freedom that has a fundamental effect on transitivity. We compute multiplex clustering coefficients for several real multiplex networks and illustrate why one must take great care when generalizing standard network concepts to multiplex networks. We also derive analytical expressions for our clustering coefficients for ensemble averages of networks in a family of random multiplex networks. Our analysis illustrates that social networks have a strong tendency to promote redundancy by closing triads at every layer and that they thereby have a different type of multiplex transitivity from transportation networks, which do not exhibit such a tendency. These insights are invisible if one only studies aggregated networks.