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Recent Developments in Quantitative Graph Theory: Information Inequalities for Networks

In this article, we tackle a challenging problem in quantitative graph theory. We establish relations between graph entropy measures representing the structural information content of networks. In particular, we prove formal relations between quantitative network measures based on Shannon's ent...

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Detalles Bibliográficos
Autores principales: Dehmer, Matthias, Sivakumar, Lavanya
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Public Library of Science 2012
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3280299/
https://www.ncbi.nlm.nih.gov/pubmed/22355362
http://dx.doi.org/10.1371/journal.pone.0031395
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author Dehmer, Matthias
Sivakumar, Lavanya
author_facet Dehmer, Matthias
Sivakumar, Lavanya
author_sort Dehmer, Matthias
collection PubMed
description In this article, we tackle a challenging problem in quantitative graph theory. We establish relations between graph entropy measures representing the structural information content of networks. In particular, we prove formal relations between quantitative network measures based on Shannon's entropy to study the relatedness of those measures. In order to establish such information inequalities for graphs, we focus on graph entropy measures based on information functionals. To prove such relations, we use known graph classes whose instances have been proven useful in various scientific areas. Our results extend the foregoing work on information inequalities for graphs.
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spelling pubmed-32802992012-02-21 Recent Developments in Quantitative Graph Theory: Information Inequalities for Networks Dehmer, Matthias Sivakumar, Lavanya PLoS One Research Article In this article, we tackle a challenging problem in quantitative graph theory. We establish relations between graph entropy measures representing the structural information content of networks. In particular, we prove formal relations between quantitative network measures based on Shannon's entropy to study the relatedness of those measures. In order to establish such information inequalities for graphs, we focus on graph entropy measures based on information functionals. To prove such relations, we use known graph classes whose instances have been proven useful in various scientific areas. Our results extend the foregoing work on information inequalities for graphs. Public Library of Science 2012-02-15 /pmc/articles/PMC3280299/ /pubmed/22355362 http://dx.doi.org/10.1371/journal.pone.0031395 Text en Dehmer, Sivakumar. http://creativecommons.org/licenses/by/4.0/ This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are properly credited.
spellingShingle Research Article
Dehmer, Matthias
Sivakumar, Lavanya
Recent Developments in Quantitative Graph Theory: Information Inequalities for Networks
title Recent Developments in Quantitative Graph Theory: Information Inequalities for Networks
title_full Recent Developments in Quantitative Graph Theory: Information Inequalities for Networks
title_fullStr Recent Developments in Quantitative Graph Theory: Information Inequalities for Networks
title_full_unstemmed Recent Developments in Quantitative Graph Theory: Information Inequalities for Networks
title_short Recent Developments in Quantitative Graph Theory: Information Inequalities for Networks
title_sort recent developments in quantitative graph theory: information inequalities for networks
topic Research Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3280299/
https://www.ncbi.nlm.nih.gov/pubmed/22355362
http://dx.doi.org/10.1371/journal.pone.0031395
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