Abstract The Random Geometric Graph (RGG) is a random graph model for network data
with an underlying spatial representation. Geometry endows RGGs with a rich dependence …

This review presents an account of the major works done on spectra of adjacency matrices
drawn on networks and the basic understanding attained so far. We have divided the review …

We investigate how the temperature calculated from the microcanonical entropy compares
with the canonical temperature for finite isolated quantum systems. We concentrate on …

We study diffusion-driven pattern formation in networks of networks, a class of multilayer
systems, where different layers have the same topology, but different internal dynamics …

The eigenvalue statistics are an important tool to capture localization to delocalization
transition in physical systems. Recently, a β-Gaussian ensemble is being proposed as a …

In this work we perform a detailed statistical analysis of topological and spectral properties of
random geometric graphs (RGGs), a graph model used to study the structure and dynamics …

We investigate the spectra of adjacency matrices of multiplex networks under random matrix
theory (RMT) framework. Through extensive numerical experiments, we demonstrate that …

Several spectral fluctuation measures of random matrix theory (RMT) have been applied in
the study of spectral properties of networks. However, the calculation of those statistics …

Within a random-matrix theory approach, we use the nearest-neighbour energy-level
spacing distribution and the entropic eigenfunction localization length to study spectral and …

We study a network model where the spatial constraint evolves with the network growth and
demonstrate that the dynamic constraint can generally cause a non-stationary multiple …