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Metric projection for dynamic multiplex networks

Evolving multiplex networks are a powerful model for representing the dynamics along time of different phenomena, such as social networks, power grids, biological pathways. However, exploring the structure of the multiplex network time series is still an open problem. Here we propose a two-step stra...

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Detalles Bibliográficos
Autor principal: Jurman, Giuseppe
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Elsevier 2016
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5011148/
https://www.ncbi.nlm.nih.gov/pubmed/27626089
http://dx.doi.org/10.1016/j.heliyon.2016.e00136
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author Jurman, Giuseppe
author_facet Jurman, Giuseppe
author_sort Jurman, Giuseppe
collection PubMed
description Evolving multiplex networks are a powerful model for representing the dynamics along time of different phenomena, such as social networks, power grids, biological pathways. However, exploring the structure of the multiplex network time series is still an open problem. Here we propose a two-step strategy to tackle this problem based on the concept of distance (metric) between networks. Given a multiplex graph, first a network of networks is built for each time step, and then a real valued time series is obtained by the sequence of (simple) networks by evaluating the distance from the first element of the series. The effectiveness of this approach in detecting the occurring changes along the original time series is shown on a synthetic example first, and then on the Gulf dataset of political events.
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spelling pubmed-50111482016-09-13 Metric projection for dynamic multiplex networks Jurman, Giuseppe Heliyon Article Evolving multiplex networks are a powerful model for representing the dynamics along time of different phenomena, such as social networks, power grids, biological pathways. However, exploring the structure of the multiplex network time series is still an open problem. Here we propose a two-step strategy to tackle this problem based on the concept of distance (metric) between networks. Given a multiplex graph, first a network of networks is built for each time step, and then a real valued time series is obtained by the sequence of (simple) networks by evaluating the distance from the first element of the series. The effectiveness of this approach in detecting the occurring changes along the original time series is shown on a synthetic example first, and then on the Gulf dataset of political events. Elsevier 2016-08-04 /pmc/articles/PMC5011148/ /pubmed/27626089 http://dx.doi.org/10.1016/j.heliyon.2016.e00136 Text en © 2016 The Author http://creativecommons.org/licenses/by/4.0/ This is an open access article under the CC BY license (http://creativecommons.org/licenses/by/4.0/).
spellingShingle Article
Jurman, Giuseppe
Metric projection for dynamic multiplex networks
title Metric projection for dynamic multiplex networks
title_full Metric projection for dynamic multiplex networks
title_fullStr Metric projection for dynamic multiplex networks
title_full_unstemmed Metric projection for dynamic multiplex networks
title_short Metric projection for dynamic multiplex networks
title_sort metric projection for dynamic multiplex networks
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5011148/
https://www.ncbi.nlm.nih.gov/pubmed/27626089
http://dx.doi.org/10.1016/j.heliyon.2016.e00136
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