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Information Entropy of Tight-Binding Random Networks with Losses and Gain: Scaling and Universality

We study the localization properties of the eigenvectors, characterized by their information entropy, of tight-binding random networks with balanced losses and gain. The random network model, which is based on Erdős–Rényi (ER) graphs, is defined by three parameters: the network size N, the network c...

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Autores principales: Martínez-Martínez, C. T., Méndez-Bermúdez, J. A.
Formato: Online Artículo Texto
Lenguaje:English
Publicado: MDPI 2019
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7514196/
https://www.ncbi.nlm.nih.gov/pubmed/33266802
http://dx.doi.org/10.3390/e21010086
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author Martínez-Martínez, C. T.
Méndez-Bermúdez, J. A.
author_facet Martínez-Martínez, C. T.
Méndez-Bermúdez, J. A.
author_sort Martínez-Martínez, C. T.
collection PubMed
description We study the localization properties of the eigenvectors, characterized by their information entropy, of tight-binding random networks with balanced losses and gain. The random network model, which is based on Erdős–Rényi (ER) graphs, is defined by three parameters: the network size N, the network connectivity [Formula: see text] , and the losses-and-gain strength [Formula: see text]. Here, N and [Formula: see text] are the standard parameters of ER graphs, while we introduce losses and gain by including complex self-loops on all vertices with the imaginary amplitude i [Formula: see text] with random balanced signs, thus breaking the Hermiticity of the corresponding adjacency matrices and inducing complex spectra. By the use of extensive numerical simulations, we define a scaling parameter [Formula: see text] that fixes the localization properties of the eigenvectors of our random network model; such that, when [Formula: see text] ([Formula: see text]), the eigenvectors are localized (extended), while the localization-to-delocalization transition occurs for [Formula: see text]. Moreover, to extend the applicability of our findings, we demonstrate that for fixed [Formula: see text] , the spectral properties (characterized by the position of the eigenvalues on the complex plane) of our network model are also universal; i.e., they do not depend on the specific values of the network parameters.
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spelling pubmed-75141962020-11-09 Information Entropy of Tight-Binding Random Networks with Losses and Gain: Scaling and Universality Martínez-Martínez, C. T. Méndez-Bermúdez, J. A. Entropy (Basel) Article We study the localization properties of the eigenvectors, characterized by their information entropy, of tight-binding random networks with balanced losses and gain. The random network model, which is based on Erdős–Rényi (ER) graphs, is defined by three parameters: the network size N, the network connectivity [Formula: see text] , and the losses-and-gain strength [Formula: see text]. Here, N and [Formula: see text] are the standard parameters of ER graphs, while we introduce losses and gain by including complex self-loops on all vertices with the imaginary amplitude i [Formula: see text] with random balanced signs, thus breaking the Hermiticity of the corresponding adjacency matrices and inducing complex spectra. By the use of extensive numerical simulations, we define a scaling parameter [Formula: see text] that fixes the localization properties of the eigenvectors of our random network model; such that, when [Formula: see text] ([Formula: see text]), the eigenvectors are localized (extended), while the localization-to-delocalization transition occurs for [Formula: see text]. Moreover, to extend the applicability of our findings, we demonstrate that for fixed [Formula: see text] , the spectral properties (characterized by the position of the eigenvalues on the complex plane) of our network model are also universal; i.e., they do not depend on the specific values of the network parameters. MDPI 2019-01-18 /pmc/articles/PMC7514196/ /pubmed/33266802 http://dx.doi.org/10.3390/e21010086 Text en © 2019 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (http://creativecommons.org/licenses/by/4.0/).
spellingShingle Article
Martínez-Martínez, C. T.
Méndez-Bermúdez, J. A.
Information Entropy of Tight-Binding Random Networks with Losses and Gain: Scaling and Universality
title Information Entropy of Tight-Binding Random Networks with Losses and Gain: Scaling and Universality
title_full Information Entropy of Tight-Binding Random Networks with Losses and Gain: Scaling and Universality
title_fullStr Information Entropy of Tight-Binding Random Networks with Losses and Gain: Scaling and Universality
title_full_unstemmed Information Entropy of Tight-Binding Random Networks with Losses and Gain: Scaling and Universality
title_short Information Entropy of Tight-Binding Random Networks with Losses and Gain: Scaling and Universality
title_sort information entropy of tight-binding random networks with losses and gain: scaling and universality
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7514196/
https://www.ncbi.nlm.nih.gov/pubmed/33266802
http://dx.doi.org/10.3390/e21010086
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