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Diffusion Dynamics and Optimal Coupling in Multiplex Networks with Directed Layers

机译:具有定向图层的多路复用网络中的扩散动态和最佳耦合

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Multiplex networks have been intensively studied during the last few years as they offer a more realistic representation of many interdependent and multilevel complex networked systems. However, even if most real networks have some degree of directionality, the vast majority of the existent literature deals with multiplex networks where all layers are undirected. Here, we study the dynamics of diffusion processes acting on coupled multilayer networks where at least one layer consists of a directed graph; we call these directed multiplex networks . We reveal a new and unexpected signature of diffusion dynamics on directed multiplex networks, namely, that different from their undirected counterparts, they can exhibit a nonmonotonic rate of convergence to steady state as a function of the degree of coupling, resulting in a faster diffusion at an intermediate degree of coupling than when the two layers are fully coupled. We use synthetic multiplex examples and real-world topologies to illustrate the characteristics of the underlying dynamics that give rise to a regime in which an optimal coupling exists. We further provide analytical and numerical evidence that this new phenomenon is solely a property of directed multiplex, where at least one of the layers exhibits sufficient directionality quantified by a normalized metric of asymmetry in directional path lengths. Given the ubiquity of both directed and multilayer networks in nature, our results have important implications for studying the dynamics of multilevel complex systems.
机译:在过去几年中,多路复用网络已经在很多年内进行了集中研究,因为它们提供了许多相互依存和多级复合网络系统的更现实的代表性。但是,即使大多数真实网络具有一定程度的方向性,绝大多数存在的文献也处理了所有层的多路复用网络。在这里,我们研究了作用于耦合多层网络的扩散过程的动态,其中至少一层由定向图组成;我们调用这些定向的多路复用网络。我们揭示了指导多路复用网络上的扩散动态的新的和意外签名,即与其无向对应物不同,它们可以表现出非调制率的收敛速率,以稳定状态是耦合程度的函数,导致更快的扩散比两层完全耦合的当中间耦合度。我们使用合成多路复用示例和现实世界拓扑来说明产生最佳耦合的制度的底层动态的特征。我们进一步提供了这种新现象的分析和数值证据,即这种新的现象是指向多路复用的性质,其中至少一个层表现出通过在方向路径长度的归一化度量的正常化度量来定量的足够的方向性。鉴于本质上的盲目和多层网络的无处不在,我们的结果对研究多级复杂系统的动态具有重要意义。

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