Iacovacci Jacopo, Bianconi Ginestra
School of Mathematical Sciences, Queen Mary University of London, Mile End Road, E1 4NS, United Kingdom, London.
Chaos. 2016 Jun;26(6):065306. doi: 10.1063/1.4953161.
Multiplex networks are generalized network structures that are able to describe networks in which the same set of nodes are connected by links that have different connotations. Multiplex networks are ubiquitous since they describe social, financial, engineering, and biological networks as well. Extending our ability to analyze complex networks to multiplex network structures increases greatly the level of information that is possible to extract from big data. For these reasons, characterizing the centrality of nodes in multiplex networks and finding new ways to solve challenging inference problems defined on multiplex networks are fundamental questions of network science. In this paper, we discuss the relevance of the Multiplex PageRank algorithm for measuring the centrality of nodes in multilayer networks and we characterize the utility of the recently introduced indicator function Θ̃(S) for describing their mesoscale organization and community structure. As working examples for studying these measures, we consider three multiplex network datasets coming for social science.
多重网络是一种广义网络结构，能够描述由具有不同内涵的链路连接同一组节点的网络。多重网络无处不在，因为它们同样描述了社会、金融、工程和生物网络。将我们分析复杂网络的能力扩展到多重网络结构，极大地提高了从大数据中提取信息的水平。出于这些原因，刻画多重网络中节点的中心性以及找到解决多重网络上具有挑战性的推理问题的新方法，是网络科学的基本问题。在本文中，我们讨论了多重PageRank算法对于测量多层网络中节点中心性的相关性，并刻画了最近引入的指标函数Θ̃(S)用于描述其介观组织和社区结构的效用。作为研究这些度量的实际示例，我们考虑了来自社会科学的三个多重网络数据集。
