Analyzing complex networks evolution through Information Theory quantifiers

A methodology to analyze dynamical changes in complex networks based on Information Theory quantifiers is proposed. The square root of the Jensen-Shannon divergence, a measure of dissimilarity between two probability distributions, and the MPR Statistical Complexity are used to quantify states in th...

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Detalles Bibliográficos
Publicado: 2011
Materias:
Complex networks
Jensen-Shannon divergence
Statistical complexity
Acceso en línea:https://bibliotecadigital.exactas.uba.ar/collection/paper/document/paper_03759601_v375_n4_p801_Carpi
http://hdl.handle.net/20.500.12110/paper_03759601_v375_n4_p801_Carpi
Aporte de:
Biblioteca Digital - Facultad de Ciencias Exactas y Naturales (UBA) de Universidad de Buenos Aires
id paper:paper_03759601_v375_n4_p801_Carpi
record_format dspace
spelling paper:paper_03759601_v375_n4_p801_Carpi2023-06-08T15:38:44Z Analyzing complex networks evolution through Information Theory quantifiers Complex networks Jensen-Shannon divergence Statistical complexity A methodology to analyze dynamical changes in complex networks based on Information Theory quantifiers is proposed. The square root of the Jensen-Shannon divergence, a measure of dissimilarity between two probability distributions, and the MPR Statistical Complexity are used to quantify states in the network evolution process. Three cases are analyzed, the Watts-Strogatz model, a gene network during the progression of Alzheimer's disease and a climate network for the Tropical Pacific region to study the El Niño/Southern Oscillation (ENSO) dynamic. We find that the proposed quantifiers are able not only to capture changes in the dynamics of the processes but also to quantify and compare states in their evolution. © 2010 Elsevier B.V. All rights reserved. 2011 https://bibliotecadigital.exactas.uba.ar/collection/paper/document/paper_03759601_v375_n4_p801_Carpi http://hdl.handle.net/20.500.12110/paper_03759601_v375_n4_p801_Carpi
institution Universidad de Buenos Aires
institution_str I-28
repository_str R-134
collection Biblioteca Digital - Facultad de Ciencias Exactas y Naturales (UBA)
topic Complex networks
Jensen-Shannon divergence
Statistical complexity
spellingShingle Complex networks
Jensen-Shannon divergence
Statistical complexity
Analyzing complex networks evolution through Information Theory quantifiers
topic_facet Complex networks
Jensen-Shannon divergence
Statistical complexity
description A methodology to analyze dynamical changes in complex networks based on Information Theory quantifiers is proposed. The square root of the Jensen-Shannon divergence, a measure of dissimilarity between two probability distributions, and the MPR Statistical Complexity are used to quantify states in the network evolution process. Three cases are analyzed, the Watts-Strogatz model, a gene network during the progression of Alzheimer's disease and a climate network for the Tropical Pacific region to study the El Niño/Southern Oscillation (ENSO) dynamic. We find that the proposed quantifiers are able not only to capture changes in the dynamics of the processes but also to quantify and compare states in their evolution. © 2010 Elsevier B.V. All rights reserved.
title Analyzing complex networks evolution through Information Theory quantifiers
title_short Analyzing complex networks evolution through Information Theory quantifiers
title_full Analyzing complex networks evolution through Information Theory quantifiers
title_fullStr Analyzing complex networks evolution through Information Theory quantifiers
title_full_unstemmed Analyzing complex networks evolution through Information Theory quantifiers
title_sort analyzing complex networks evolution through information theory quantifiers
publishDate 2011
url https://bibliotecadigital.exactas.uba.ar/collection/paper/document/paper_03759601_v375_n4_p801_Carpi
http://hdl.handle.net/20.500.12110/paper_03759601_v375_n4_p801_Carpi
_version_ 1768542507023466496

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