Departament d'Enginyeria Informàtica i Matemàtiques, Universitat Rovira i Virgili, 43007 Tarragona, Spain.
1] Departament d'Enginyeria Informàtica i Matemàtiques, Universitat Rovira i Virgili, 43007 Tarragona, Spain [2] LaTTiCe-CNRS, École Normale Supérieure, 75005 Paris, France [3] Institut des Systèmes Complexes - Paris Île-de-France (ISC-PIF), 75013 Paris, France.
Nat Commun. 2015 Apr 23;6:6868. doi: 10.1038/ncomms7868.
The determination of the most central agents in complex networks is important because they are responsible for a faster propagation of information, epidemics, failures and congestion, among others. A challenging problem is to identify them in networked systems characterized by different types of interactions, forming interconnected multilayer networks. Here we describe a mathematical framework that allows us to calculate centrality in such networks and rank nodes accordingly, finding the ones that play the most central roles in the cohesion of the whole structure, bridging together different types of relations. These nodes are the most versatile in the multilayer network. We investigate empirical interconnected multilayer networks and show that the approaches based on aggregating--or neglecting--the multilayer structure lead to a wrong identification of the most versatile nodes, overestimating the importance of more marginal agents and demonstrating the power of versatility in predicting their role in diffusive and congestion processes.
确定复杂网络中最核心的节点非常重要，因为它们是信息快速传播、传染病爆发、故障和拥塞等现象的主要原因。一个具有挑战性的问题是，在具有不同类型相互作用的网络系统中识别它们，这些系统形成了相互连接的多层网络。在这里，我们描述了一个数学框架，该框架允许我们在这种网络中计算核心度，并根据节点的核心度进行排序，从而找到在整个结构的凝聚中起最重要作用的节点，将不同类型的关系联系在一起。这些节点在多层网络中是最通用的。我们研究了经验相互连接的多层网络，并表明基于聚合（或忽略）多层结构的方法会错误地识别最通用的节点，高估了更边缘节点的重要性，并展示了通用性在预测它们在扩散和拥塞过程中的作用方面的强大能力。
