Shannon entropy in time-varying semantic networks of titles of scientific paper

Abstract Recent work has employed information theory in social and complex networks. Studies often discuss entropy in the degree distributions of a network. However, no specific work on entropy exists in clique networks. This work is an extension of a previous study that discussed this topic. We pro...

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Main Authors: Marcelo do Vale Cunha, Carlos Cesar Ribeiro Santos, Marcelo Albano Moret, Hernane Borges de Barros Pereira
Format: Article
Language:English
Published: SpringerOpen 2020-08-01
Series:Applied Network Science
Subjects:
Online Access:http://link.springer.com/article/10.1007/s41109-020-00292-0
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spelling doaj-57d6882a9034412f95922412719254462020-11-25T03:30:17ZengSpringerOpenApplied Network Science2364-82282020-08-015111710.1007/s41109-020-00292-0Shannon entropy in time-varying semantic networks of titles of scientific paperMarcelo do Vale Cunha0Carlos Cesar Ribeiro Santos1Marcelo Albano Moret2Hernane Borges de Barros Pereira3Programa de Modelagem Computacional, Centro Universitário Senai CimatecPrograma de Modelagem Computacional, Centro Universitário Senai CimatecPrograma de Modelagem Computacional, Centro Universitário Senai CimatecPrograma de Modelagem Computacional, Centro Universitário Senai CimatecAbstract Recent work has employed information theory in social and complex networks. Studies often discuss entropy in the degree distributions of a network. However, no specific work on entropy exists in clique networks. This work is an extension of a previous study that discussed this topic. We propose a method for calculating the entropy of a clique network and its minimum and maximum values in temporal semantic networks based on titles of scientific papers. In addition, the critical network of moments was extracted. We use the titles of scientific papers published in Nature and Science over ten-year period. The results show the diversity of vocabulary over time, based on the entropy values of vertices and edges. In each critical network, we discover the paths that connect important words and an interesting modular structure.http://link.springer.com/article/10.1007/s41109-020-00292-0Networks of cliquesShannon entropyTime–varying graphsSemantic networksNetwork theory
collection DOAJ
language English
format Article
sources DOAJ
author Marcelo do Vale Cunha
Carlos Cesar Ribeiro Santos
Marcelo Albano Moret
Hernane Borges de Barros Pereira
spellingShingle Marcelo do Vale Cunha
Carlos Cesar Ribeiro Santos
Marcelo Albano Moret
Hernane Borges de Barros Pereira
Shannon entropy in time-varying semantic networks of titles of scientific paper
Applied Network Science
Networks of cliques
Shannon entropy
Time–varying graphs
Semantic networks
Network theory
author_facet Marcelo do Vale Cunha
Carlos Cesar Ribeiro Santos
Marcelo Albano Moret
Hernane Borges de Barros Pereira
author_sort Marcelo do Vale Cunha
title Shannon entropy in time-varying semantic networks of titles of scientific paper
title_short Shannon entropy in time-varying semantic networks of titles of scientific paper
title_full Shannon entropy in time-varying semantic networks of titles of scientific paper
title_fullStr Shannon entropy in time-varying semantic networks of titles of scientific paper
title_full_unstemmed Shannon entropy in time-varying semantic networks of titles of scientific paper
title_sort shannon entropy in time-varying semantic networks of titles of scientific paper
publisher SpringerOpen
series Applied Network Science
issn 2364-8228
publishDate 2020-08-01
description Abstract Recent work has employed information theory in social and complex networks. Studies often discuss entropy in the degree distributions of a network. However, no specific work on entropy exists in clique networks. This work is an extension of a previous study that discussed this topic. We propose a method for calculating the entropy of a clique network and its minimum and maximum values in temporal semantic networks based on titles of scientific papers. In addition, the critical network of moments was extracted. We use the titles of scientific papers published in Nature and Science over ten-year period. The results show the diversity of vocabulary over time, based on the entropy values of vertices and edges. In each critical network, we discover the paths that connect important words and an interesting modular structure.
topic Networks of cliques
Shannon entropy
Time–varying graphs
Semantic networks
Network theory
url http://link.springer.com/article/10.1007/s41109-020-00292-0
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