Sparse non-Hermitian random matrices arise in the study of disordered physical systems
with asymmetric local interactions, and have applications ranging from neural networks to …

Generating random networks efficiently and accurately is an important challenge for
practical applications, and an interesting question for theoretical study. This book presents …

A unique demographic shift towards urban centres has necessitated incorporation of
sustainability principles in the tenets of urban infrastructure planning and design. Adopting …

Although the spectra of random networks have been studied for a long time, the influence of
network topology on the dense limit of network spectra remains poorly understood. By …

Using methods from random matrix theory researchers have recently calculated the full
spectra of random networks with arbitrary degrees and with community structure. Both reveal …

The spectral and localization properties of heterogeneous random graphs are determined
by the resolvent distributional equations, which have so far resisted an analytic treatment …

We review the problem of how to compute the spectral density of sparse symmetric random
matrices, ie weighted adjacency matrices of undirected graphs. Starting from the Edwards …

We present a linear stability analysis of stationary states (or fixed points) in large dynamical
systems defined on random, directed graphs with a prescribed distribution of indegrees and …

The spectrum of the adjacency matrix plays several important roles in the mathematical
theory of networks and network data analysis, for example in percolation theory, community …

We develop a thorough analytical study of the O (1/N) correction to the spectrum of regular
random graphs with N→∞ nodes. The finite-size fluctuations of the resolvent are given in …