Coevolution and correlated multiplexity in multiplex networks

TL;DR

This paper introduces a model for multiplex networks where layers coevolve, revealing how this coevolution induces degree correlations and affects dynamical processes like cascade susceptibility.

Contribution

It presents a novel framework for understanding coevolution in multiplex networks and its impact on network structure and dynamics.

Findings

Coevolution induces strong degree correlations across layers.

Degree distributions are modulated by coevolution.

Co-evolution reduces susceptibility to cascade processes.

Abstract

Distinct channels of interaction in a complex networked system define network layers, which co-exist and co-operate for the system's function. Towards realistic modeling and understanding such multiplex systems, we introduce and study a class of growing multiplex network models in which different network layers coevolve, and examine how the entangled growth of coevolving layers can shape the overall network structure. We show analytically and numerically that the coevolution can induce strong degree correlations across layers, as well as modulate degree distributions. We further show that such a coevolution-induced correlated multiplexity can alter the system's response to dynamical process, exemplified by the suppressed susceptibility to a threshold cascade process.