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Eigencentrality based on dissimilarity measures reveals central nodes in complex networks

One of the most important problems in complex network’s theory is the location of the entities that are essential or have a main role within the network. For this purpose, the use of dissimilarity measures (specific to theory of classification and data mining) to enrich the centrality measures in co...

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Autores principales: Alvarez-Socorro, A. J., Herrera-Almarza, G. C., González-Díaz, L. A.
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Nature Publishing Group 2015
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4658528/
https://www.ncbi.nlm.nih.gov/pubmed/26603652
http://dx.doi.org/10.1038/srep17095
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author Alvarez-Socorro, A. J.
Herrera-Almarza, G. C.
González-Díaz, L. A.
author_facet Alvarez-Socorro, A. J.
Herrera-Almarza, G. C.
González-Díaz, L. A.
author_sort Alvarez-Socorro, A. J.
collection PubMed
description One of the most important problems in complex network’s theory is the location of the entities that are essential or have a main role within the network. For this purpose, the use of dissimilarity measures (specific to theory of classification and data mining) to enrich the centrality measures in complex networks is proposed. The centrality method used is the eigencentrality which is based on the heuristic that the centrality of a node depends on how central are the nodes in the immediate neighbourhood (like rich get richer phenomenon). This can be described by an eigenvalues problem, however the information of the neighbourhood and the connections between neighbours is not taken in account, neglecting their relevance when is one evaluates the centrality/importance/influence of a node. The contribution calculated by the dissimilarity measure is parameter independent, making the proposed method is also parameter independent. Finally, we perform a comparative study of our method versus other methods reported in the literature, obtaining more accurate and less expensive computational results in most cases.
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spelling pubmed-46585282015-11-30 Eigencentrality based on dissimilarity measures reveals central nodes in complex networks Alvarez-Socorro, A. J. Herrera-Almarza, G. C. González-Díaz, L. A. Sci Rep Article One of the most important problems in complex network’s theory is the location of the entities that are essential or have a main role within the network. For this purpose, the use of dissimilarity measures (specific to theory of classification and data mining) to enrich the centrality measures in complex networks is proposed. The centrality method used is the eigencentrality which is based on the heuristic that the centrality of a node depends on how central are the nodes in the immediate neighbourhood (like rich get richer phenomenon). This can be described by an eigenvalues problem, however the information of the neighbourhood and the connections between neighbours is not taken in account, neglecting their relevance when is one evaluates the centrality/importance/influence of a node. The contribution calculated by the dissimilarity measure is parameter independent, making the proposed method is also parameter independent. Finally, we perform a comparative study of our method versus other methods reported in the literature, obtaining more accurate and less expensive computational results in most cases. Nature Publishing Group 2015-11-25 /pmc/articles/PMC4658528/ /pubmed/26603652 http://dx.doi.org/10.1038/srep17095 Text en Copyright © 2015, Macmillan Publishers Limited http://creativecommons.org/licenses/by/4.0/ This work is licensed under a Creative Commons Attribution 4.0 International License. The images or other third party material in this article are included in the article’s Creative Commons license, unless indicated otherwise in the credit line; if the material is not included under the Creative Commons license, users will need to obtain permission from the license holder to reproduce the material. To view a copy of this license, visit http://creativecommons.org/licenses/by/4.0/
spellingShingle Article
Alvarez-Socorro, A. J.
Herrera-Almarza, G. C.
González-Díaz, L. A.
Eigencentrality based on dissimilarity measures reveals central nodes in complex networks
title Eigencentrality based on dissimilarity measures reveals central nodes in complex networks
title_full Eigencentrality based on dissimilarity measures reveals central nodes in complex networks
title_fullStr Eigencentrality based on dissimilarity measures reveals central nodes in complex networks
title_full_unstemmed Eigencentrality based on dissimilarity measures reveals central nodes in complex networks
title_short Eigencentrality based on dissimilarity measures reveals central nodes in complex networks
title_sort eigencentrality based on dissimilarity measures reveals central nodes in complex networks
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4658528/
https://www.ncbi.nlm.nih.gov/pubmed/26603652
http://dx.doi.org/10.1038/srep17095
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