Spectral analysis of deformed random networks

TL;DR

This paper investigates how the spectral properties of random networks change as they transition from perfect community structures to fully deformed networks, revealing key statistical behaviors and applying findings to biological networks.

Contribution

It provides a detailed analysis of spectral transitions in deformed random networks and demonstrates the utility of spectral measures in detecting network deformations, including biological networks.

Findings

Spacing distribution transitions from Poisson to GOE statistics.

Eigenvalue density approaches Wigner's semicircular law with deformation.

Spectral rigidity varies with deformation, useful for detecting small changes.

Abstract

We study spectral behavior of sparsely connected random networks under the random matrix framework. Sub-networks without any connection among them form a network having perfect community structure. As connections among the sub-networks are introduced, the spacing distribution shows a transition from the Poisson statistics to the Gaussian orthogonal ensemble statistics of random matrix theory. The eigenvalue density distribution shows a transition to the Wigner's semicircular behavior for a completely deformed network. The range for which spectral rigidity, measured by the Dyson-Mehta $\Delta_3$ statistics, follows the Gaussian orthogonal ensemble statistics depends upon the deformation of the network from the perfect community structure. The spacing distribution is particularly useful to track very slight deformations of the network from a perfect community structure, whereas the density distribution and the $\Delta_3$ statistics remain identical to the undeformed network. On the other hand the $\Delta_3$ statistics is useful for the larger deformation strengths. Finally, we analyze the spectrum of a protein-protein interaction network for Helicobacter, and compare the spectral behavior with those of the model networks.