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A coevolving model based on preferential triadic closure for social media networks
The dynamical origin of complex networks, i.e., the underlying principles governing network evolution, is a crucial issue in network study. In this paper, by carrying out analysis to the temporal data of Flickr and Epinions–two typical social media networks, we found that the dynamical pattern in ne...
Autores principales: | , , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Nature Publishing Group
2013
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3753589/ https://www.ncbi.nlm.nih.gov/pubmed/23979061 http://dx.doi.org/10.1038/srep02512 |
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author | Li, Menghui Zou, Hailin Guan, Shuguang Gong, Xiaofeng Li, Kun Di, Zengru Lai, Choy-Heng |
author_facet | Li, Menghui Zou, Hailin Guan, Shuguang Gong, Xiaofeng Li, Kun Di, Zengru Lai, Choy-Heng |
author_sort | Li, Menghui |
collection | PubMed |
description | The dynamical origin of complex networks, i.e., the underlying principles governing network evolution, is a crucial issue in network study. In this paper, by carrying out analysis to the temporal data of Flickr and Epinions–two typical social media networks, we found that the dynamical pattern in neighborhood, especially the formation of triadic links, plays a dominant role in the evolution of networks. We thus proposed a coevolving dynamical model for such networks, in which the evolution is only driven by the local dynamics–the preferential triadic closure. Numerical experiments verified that the model can reproduce global properties which are qualitatively consistent with the empirical observations. |
format | Online Article Text |
id | pubmed-3753589 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2013 |
publisher | Nature Publishing Group |
record_format | MEDLINE/PubMed |
spelling | pubmed-37535892013-08-27 A coevolving model based on preferential triadic closure for social media networks Li, Menghui Zou, Hailin Guan, Shuguang Gong, Xiaofeng Li, Kun Di, Zengru Lai, Choy-Heng Sci Rep Article The dynamical origin of complex networks, i.e., the underlying principles governing network evolution, is a crucial issue in network study. In this paper, by carrying out analysis to the temporal data of Flickr and Epinions–two typical social media networks, we found that the dynamical pattern in neighborhood, especially the formation of triadic links, plays a dominant role in the evolution of networks. We thus proposed a coevolving dynamical model for such networks, in which the evolution is only driven by the local dynamics–the preferential triadic closure. Numerical experiments verified that the model can reproduce global properties which are qualitatively consistent with the empirical observations. Nature Publishing Group 2013-08-27 /pmc/articles/PMC3753589/ /pubmed/23979061 http://dx.doi.org/10.1038/srep02512 Text en Copyright © 2013, Macmillan Publishers Limited. All rights reserved http://creativecommons.org/licenses/by-nc-nd/3.0/ This work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivs 3.0 Unported License. To view a copy of this license, visit http://creativecommons.org/licenses/by-nc-nd/3.0/ |
spellingShingle | Article Li, Menghui Zou, Hailin Guan, Shuguang Gong, Xiaofeng Li, Kun Di, Zengru Lai, Choy-Heng A coevolving model based on preferential triadic closure for social media networks |
title | A coevolving model based on preferential triadic closure for social media networks |
title_full | A coevolving model based on preferential triadic closure for social media networks |
title_fullStr | A coevolving model based on preferential triadic closure for social media networks |
title_full_unstemmed | A coevolving model based on preferential triadic closure for social media networks |
title_short | A coevolving model based on preferential triadic closure for social media networks |
title_sort | coevolving model based on preferential triadic closure for social media networks |
topic | Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3753589/ https://www.ncbi.nlm.nih.gov/pubmed/23979061 http://dx.doi.org/10.1038/srep02512 |
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