Simulating the dynamics of scale-free networks via optimization.

We deal here with the issue of complex network evolution. The analysis of topological evolution of complex networks plays a crucial role in predicting their future. While an impressive amount of work has been done on the issue, very little attention has been so far devoted to the investigation of ho...

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Autores principales:	Tiago Alves Schieber, Martín Gómez Ravetti
Formato:	article
Lenguaje:	EN
Publicado:	Public Library of Science (PLoS) 2013
Materias:	Medicine R Science Q
Acceso en línea:	https://doaj.org/article/a5bb24940ab24b6db36f42da6407480f
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id	oai:doaj.org-article:a5bb24940ab24b6db36f42da6407480f
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spelling	oai:doaj.org-article:a5bb24940ab24b6db36f42da6407480f2021-11-18T08:42:59ZSimulating the dynamics of scale-free networks via optimization.1932-620310.1371/journal.pone.0080783https://doaj.org/article/a5bb24940ab24b6db36f42da6407480f2013-01-01T00:00:00Zhttps://www.ncbi.nlm.nih.gov/pmc/articles/pmid/24353752/pdf/?tool=EBIhttps://doaj.org/toc/1932-6203We deal here with the issue of complex network evolution. The analysis of topological evolution of complex networks plays a crucial role in predicting their future. While an impressive amount of work has been done on the issue, very little attention has been so far devoted to the investigation of how information theory quantifiers can be applied to characterize networks evolution. With the objective of dynamically capture the topological changes of a network's evolution, we propose a model able to quantify and reproduce several characteristics of a given network, by using the square root of the Jensen-Shannon divergence in combination with the mean degree and the clustering coefficient. To support our hypothesis, we test the model by copying the evolution of well-known models and real systems. The results show that the methodology was able to mimic the test-networks. By using this copycat model, the user is able to analyze the networks behavior over time, and also to conjecture about the main drivers of its evolution, also providing a framework to predict its evolution.Tiago Alves SchieberMartín Gómez RavettiPublic Library of Science (PLoS)articleMedicineRScienceQENPLoS ONE, Vol 8, Iss 12, p e80783 (2013)
institution	DOAJ
collection	DOAJ
language	EN
topic	Medicine R Science Q
spellingShingle	Medicine R Science Q Tiago Alves Schieber Martín Gómez Ravetti Simulating the dynamics of scale-free networks via optimization.
description	We deal here with the issue of complex network evolution. The analysis of topological evolution of complex networks plays a crucial role in predicting their future. While an impressive amount of work has been done on the issue, very little attention has been so far devoted to the investigation of how information theory quantifiers can be applied to characterize networks evolution. With the objective of dynamically capture the topological changes of a network's evolution, we propose a model able to quantify and reproduce several characteristics of a given network, by using the square root of the Jensen-Shannon divergence in combination with the mean degree and the clustering coefficient. To support our hypothesis, we test the model by copying the evolution of well-known models and real systems. The results show that the methodology was able to mimic the test-networks. By using this copycat model, the user is able to analyze the networks behavior over time, and also to conjecture about the main drivers of its evolution, also providing a framework to predict its evolution.
format	article
author	Tiago Alves Schieber Martín Gómez Ravetti
author_facet	Tiago Alves Schieber Martín Gómez Ravetti
author_sort	Tiago Alves Schieber
title	Simulating the dynamics of scale-free networks via optimization.
title_short	Simulating the dynamics of scale-free networks via optimization.
title_full	Simulating the dynamics of scale-free networks via optimization.
title_fullStr	Simulating the dynamics of scale-free networks via optimization.
title_full_unstemmed	Simulating the dynamics of scale-free networks via optimization.
title_sort	simulating the dynamics of scale-free networks via optimization.
publisher	Public Library of Science (PLoS)
publishDate	2013
url	https://doaj.org/article/a5bb24940ab24b6db36f42da6407480f
work_keys_str_mv	AT tiagoalvesschieber simulatingthedynamicsofscalefreenetworksviaoptimization AT martingomezravetti simulatingthedynamicsofscalefreenetworksviaoptimization
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Simulating the dynamics of scale-free networks via optimization.

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