标题： 相关多重性和多重随机网络的连通性 显示英文标题

标题： Correlated multiplexity and connectivity of multiplex random networks

摘要： 复杂网络系统中的节点通常会参与多种类型的交互；它们形成一个具有多种类型链接的多层网络。 在现实世界中的复杂系统中，一个节点对于一种类型链接的度和对于另一种类型链接的度并不是随机分布的，而是相关的，我们称之为相关多层性。 在本文中，我们研究了一个简单的多层随机网络模型，并证明相关多层性可以显著影响网络中巨大组件的特性。 具体来说，当双层Erdos-Renyi网络中节点在不同交互中的度数完全相关时，网络在任何非零的链接密度下都包含巨大组件。 相反，当节点的度数完全反相关时，巨大组件的出现会被显著延迟，但整个网络会在有限的链接密度下连通成一个单一组件。 我们还讨论了混合模式以及相关多层性不完美的情形。 显示英文摘要

摘要： Nodes in a complex networked system often engage in more than one type of interactions among them; they form a multiplex network with multiple types of links. In real-world complex systems, a node's degree for one type of links and that for the other are not randomly distributed but correlated, which we term correlated multiplexity. In this paper we study a simple model of multiplex random networks and demonstrate that the correlated multiplexity can drastically affect the properties of giant component in the network. Specifically, when the degrees of a node for different interactions in a duplex Erdos-Renyi network are maximally correlated, the network contains the giant component for any nonzero link densities. In contrast, when the degrees of a node are maximally anti-correlated, the emergence of giant component is significantly delayed, yet the entire network becomes connected into a single component at a finite link density. We also discuss the mixing patterns and the cases with imperfect correlated multiplexity.