Universality in Complex Networks: Random Matrix Analysis
Adaptation and Self-Organizing Systems
2016-09-08 v2 Disordered Systems and Neural Networks
Other Condensed Matter
Statistical Mechanics
Computational Physics
Molecular Networks
Other Quantitative Biology
Abstract
We apply random matrix theory to complex networks. We show that nearest neighbor spacing distribution of the eigenvalues of the adjacency matrices of various model networks, namely scale-free, small-world and random networks follow universal Gaussian orthogonal ensemble statistics of random matrix theory. Secondly we show an analogy between the onset of small-world behavior, quantified by the structural properties of networks, and the transition from Poisson to Gaussian orthogonal ensemble statistics, quantified by Brody parameter characterizing a spectral property. We also present our analysis for a protein-protein interaction network in budding yeast.
Cite
@article{arxiv.nlin/0608028,
title = {Universality in Complex Networks: Random Matrix Analysis},
author = {Jayendra N. Bandyopadhyay and Sarika Jalan},
journal= {arXiv preprint arXiv:nlin/0608028},
year = {2016}
}
Comments
4+ pages, 4 figures, to appear in PRE, major change in the paper including title