Most real networks are too large or they are not available for real time analysis. Therefore, in
practice, decisions are made based on partial information about the ground truth network. It …

Previous literature on random matrix and network science has traditionally employed
measures derived from nearest-neighbor level spacing distributions to characterize the …

Singular-value statistics (SVS) has been recently presented as a random matrix theory tool
able to properly characterize non-Hermitian random matrix ensembles [PRX Quantum 4 …

Here we introduce the non-Hermitian diluted banded random matrix (nHdBRM) ensemble
as the set of N× N real nonsymmetric matrices whose entries are independent Gaussian …

Applied network science often involves preprocessing network data before applying a
network-analysis method, and there is typically a theoretical disconnect between these …

In this work we analyze structural and spectral properties of a model of directed random
geometric graphs: given n vertices uniformly and independently distributed on the unit …

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 …

Eigenvalues statistics of various many-body systems have been widely studied using the
nearest neighbor spacing distribution under the random matrix theory framework. Here, we …

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 …

Motivated by the wide presence of multilayer networks in both natural and human-made
systems, within a random matrix theory (RMT) approach, in this study we compute …