Random matrix analysis of multiplex networks
Abstract
We investigate the spectra of adjacency matrices of multiplex networks under random matrix theory (RMT) framework. Through extensive numerical experiments, we demonstrate that upon multiplexing two random networks, the spectra of the combined multiplex network exhibit superposition of two Gaussian orthogonal ensemble (GOE)s for very small multiplexing strength followed by a smooth transition to the GOE statistics with an increase in the multiplexing strength. Interestingly, randomness in the connection architecture, introduced by random rewiring to 1D lattice, of at least one layer may govern nearest neighbor spacing distribution (NNSD) of the entire multiplex network, and in fact, can drive to a transition from the Poisson to the GOE statistics or vice versa. Notably, this transition transpires for a very small number of the random rewiring corresponding to the smallworld transition. Ergo, only one layer being represented by the smallworld network is enough to yield GOE statistics for the entire multiplex network. Spectra of adjacency matrices of underlying interaction networks have been contemplated to be related with dynamical behavior of the corresponding complex systems, the investigations presented here have implications in achieving better structural and dynamical control to the systems represented by multiplex networks against structural perturbation in only one of the layers.
 Publication:

Physica A Statistical Mechanics and its Applications
 Pub Date:
 January 2022
 DOI:
 10.1016/j.physa.2021.126457
 arXiv:
 arXiv:2107.09307
 Bibcode:
 2022PhyA..58626457R
 Keywords:

 Complex network;
 Eigenvalues;
 RMT;
 Condensed Matter  Statistical Mechanics;
 Nonlinear Sciences  Adaptation and SelfOrganizing Systems
 EPrint:
 20 pages, 14 figures