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Mapping temporal-network percolation to weighted, static event graphs

The dynamics of diffusion-like processes on temporal networks are influenced by correlations in the times of contacts. This influence is particularly strong for processes where the spreading agent has a limited lifetime at nodes: disease spreading (recovery time), diffusion of rumors (lifetime of in...

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Autores principales: Kivelä, Mikko, Cambe, Jordan, Saramäki, Jari, Karsai, Márton
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Nature Publishing Group UK 2018
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6098025/
https://www.ncbi.nlm.nih.gov/pubmed/30120278
http://dx.doi.org/10.1038/s41598-018-29577-2
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author Kivelä, Mikko
Cambe, Jordan
Saramäki, Jari
Karsai, Márton
author_facet Kivelä, Mikko
Cambe, Jordan
Saramäki, Jari
Karsai, Márton
author_sort Kivelä, Mikko
collection PubMed
description The dynamics of diffusion-like processes on temporal networks are influenced by correlations in the times of contacts. This influence is particularly strong for processes where the spreading agent has a limited lifetime at nodes: disease spreading (recovery time), diffusion of rumors (lifetime of information), and passenger routing (maximum acceptable time between transfers). We introduce weighted event graphs as a powerful and fast framework for studying connectivity determined by time-respecting paths where the allowed waiting times between contacts have an upper limit. We study percolation on the weighted event graphs and in the underlying temporal networks, with simulated and real-world networks. We show that this type of temporal-network percolation is analogous to directed percolation, and that it can be characterized by multiple order parameters.
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spelling pubmed-60980252018-08-23 Mapping temporal-network percolation to weighted, static event graphs Kivelä, Mikko Cambe, Jordan Saramäki, Jari Karsai, Márton Sci Rep Article The dynamics of diffusion-like processes on temporal networks are influenced by correlations in the times of contacts. This influence is particularly strong for processes where the spreading agent has a limited lifetime at nodes: disease spreading (recovery time), diffusion of rumors (lifetime of information), and passenger routing (maximum acceptable time between transfers). We introduce weighted event graphs as a powerful and fast framework for studying connectivity determined by time-respecting paths where the allowed waiting times between contacts have an upper limit. We study percolation on the weighted event graphs and in the underlying temporal networks, with simulated and real-world networks. We show that this type of temporal-network percolation is analogous to directed percolation, and that it can be characterized by multiple order parameters. Nature Publishing Group UK 2018-08-17 /pmc/articles/PMC6098025/ /pubmed/30120278 http://dx.doi.org/10.1038/s41598-018-29577-2 Text en © The Author(s) 2018 Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons license, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons license and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this license, visit http://creativecommons.org/licenses/by/4.0/.
spellingShingle Article
Kivelä, Mikko
Cambe, Jordan
Saramäki, Jari
Karsai, Márton
Mapping temporal-network percolation to weighted, static event graphs
title Mapping temporal-network percolation to weighted, static event graphs
title_full Mapping temporal-network percolation to weighted, static event graphs
title_fullStr Mapping temporal-network percolation to weighted, static event graphs
title_full_unstemmed Mapping temporal-network percolation to weighted, static event graphs
title_short Mapping temporal-network percolation to weighted, static event graphs
title_sort mapping temporal-network percolation to weighted, static event graphs
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6098025/
https://www.ncbi.nlm.nih.gov/pubmed/30120278
http://dx.doi.org/10.1038/s41598-018-29577-2
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