ﻻ يوجد ملخص باللغة العربية
Multilayer networks are widespread in natural and manmade systems. Key properties of these networks are their spectral and eigenfunction characteristics, as they determine the critical properties of many dynamics occurring on top of them. In this paper, we numerically demonstrate that the normalized localization length $beta$ of the eigenfunctions of multilayer random networks follows a simple scaling law given by $beta=x^*/(1+x^*)$, with $x^*=gamma(b_{text{eff}}^2/L)^delta$, $gamma,deltasim 1$ and $b_{text{eff}}$ being the effective bandwidth of the adjacency matrix of the network, whose size is $L=Mtimes N$. The reported scaling law for $beta$ might help to better understand criticality in multilayer networks as well as to predict the eigenfunction localization properties of them.
Multilayer networks represent systems in which there are several topological levels each one representing one kind of interaction or interdependency between the systems elements. These networks have attracted a lot of attention recently because their
The controllability of a network is a theoretical problem of relevance in a variety of contexts ranging from financial markets to the brain. Until now, network controllability has been characterized only on isolated networks, while the vast majority
The formation of network structure is mainly influenced by an individual nodes activity and its memory, where activity can usually be interpreted as the individual inherent property and memory can be represented by the interaction strength between no
Designing an efficient routing strategy is of great importance to alleviate traffic congestion in multilayer networks. In this work, we design an effective routing strategy for multilayer networks by comprehensively considering the roles of nodes loc
To improve the accuracy of network-based SIS models we introduce and study a multilayer representation of a time-dependent network. In particular, we assume that individuals have their long-term (permanent) contacts that are always present, identifyi