Localization transition, Lifschitz tails and rare-region effects in network models

TL;DR

This paper investigates how heterogeneity affects localization and rare-region effects in network models, linking spectral properties to phase transitions and Griffiths phases in various network topologies.

Contribution

It introduces a quenched mean-field approach to analyze localization, Lifschitz tails, and rare-region effects, providing new insights into phase transitions in complex networks.

Findings

Localization is characterized by inverse participation ratio distributions.

Lifschitz tails relate to spectral density and order parameters.

Griffiths Phases are confirmed on regular and small-world networks.

Abstract

Effects of heterogeneity in the suspected-infected-susceptible model on networks are investigated using quenched mean-field theory. The emergence of localization is described by the distributions of the inverse participation ratio and compared with the rare-region effects appearing in simulations and in the Lifschitz tails. The latter, in the linear approximation, is related to the spectral density of the Laplacian matrix and to the time dependent order parameter. I show that these approximations indicate correctly Griffiths Phases both on regular one-dimensional lattices and on small world networks exhibiting purely topological disorder. I discuss the localization transition that occurs on scale-free networks at $\gamma=3$ degree exponent.