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Modelling sequences and temporal networks with dynamic community structures

In evolving complex systems such as air traffic and social organisations, collective effects emerge from their many components’ dynamic interactions. While the dynamic interactions can be represented by temporal networks with nodes and links that change over time, they remain highly complex. It is t...

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Detalles Bibliográficos
Autores principales: Peixoto, Tiago P., Rosvall, Martin
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Nature Publishing Group UK 2017
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5605535/
https://www.ncbi.nlm.nih.gov/pubmed/28928409
http://dx.doi.org/10.1038/s41467-017-00148-9
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author Peixoto, Tiago P.
Rosvall, Martin
author_facet Peixoto, Tiago P.
Rosvall, Martin
author_sort Peixoto, Tiago P.
collection PubMed
description In evolving complex systems such as air traffic and social organisations, collective effects emerge from their many components’ dynamic interactions. While the dynamic interactions can be represented by temporal networks with nodes and links that change over time, they remain highly complex. It is therefore often necessary to use methods that extract the temporal networks’ large-scale dynamic community structure. However, such methods are subject to overfitting or suffer from effects of arbitrary, a priori-imposed timescales, which should instead be extracted from data. Here we simultaneously address both problems and develop a principled data-driven method that determines relevant timescales and identifies patterns of dynamics that take place on networks, as well as shape the networks themselves. We base our method on an arbitrary-order Markov chain model with community structure, and develop a nonparametric Bayesian inference framework that identifies the simplest such model that can explain temporal interaction data.
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spelling pubmed-56055352017-09-22 Modelling sequences and temporal networks with dynamic community structures Peixoto, Tiago P. Rosvall, Martin Nat Commun Article In evolving complex systems such as air traffic and social organisations, collective effects emerge from their many components’ dynamic interactions. While the dynamic interactions can be represented by temporal networks with nodes and links that change over time, they remain highly complex. It is therefore often necessary to use methods that extract the temporal networks’ large-scale dynamic community structure. However, such methods are subject to overfitting or suffer from effects of arbitrary, a priori-imposed timescales, which should instead be extracted from data. Here we simultaneously address both problems and develop a principled data-driven method that determines relevant timescales and identifies patterns of dynamics that take place on networks, as well as shape the networks themselves. We base our method on an arbitrary-order Markov chain model with community structure, and develop a nonparametric Bayesian inference framework that identifies the simplest such model that can explain temporal interaction data. Nature Publishing Group UK 2017-09-19 /pmc/articles/PMC5605535/ /pubmed/28928409 http://dx.doi.org/10.1038/s41467-017-00148-9 Text en © The Author(s) 2017 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
Peixoto, Tiago P.
Rosvall, Martin
Modelling sequences and temporal networks with dynamic community structures
title Modelling sequences and temporal networks with dynamic community structures
title_full Modelling sequences and temporal networks with dynamic community structures
title_fullStr Modelling sequences and temporal networks with dynamic community structures
title_full_unstemmed Modelling sequences and temporal networks with dynamic community structures
title_short Modelling sequences and temporal networks with dynamic community structures
title_sort modelling sequences and temporal networks with dynamic community structures
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5605535/
https://www.ncbi.nlm.nih.gov/pubmed/28928409
http://dx.doi.org/10.1038/s41467-017-00148-9
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