Cargando…
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...
Autores principales: | , , |
---|---|
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 |
_version_ | 1782402527340265472 |
---|---|
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. |
format | Online Article Text |
id | pubmed-4658528 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2015 |
publisher | Nature Publishing Group |
record_format | MEDLINE/PubMed |
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 |
work_keys_str_mv | AT alvarezsocorroaj eigencentralitybasedondissimilaritymeasuresrevealscentralnodesincomplexnetworks AT herreraalmarzagc eigencentralitybasedondissimilaritymeasuresrevealscentralnodesincomplexnetworks AT gonzalezdiazla eigencentralitybasedondissimilaritymeasuresrevealscentralnodesincomplexnetworks |