Visibility graph based temporal community detection with applications in biological time series

Abstract Temporal behavior is an essential aspect of all biological systems. Time series have been previously represented as networks. Such representations must address two fundamental problems on how to: (1) Create appropriate networks to reflect the characteristics of biological time series. (2) D...

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Autores principales:	Minzhang Zheng, Sergii Domanskyi, Carlo Piermarocchi, George I. Mias
Formato:	article
Lenguaje:	EN
Publicado:	Nature Portfolio 2021
Materias:	Medicine R Science Q
Acceso en línea:	https://doaj.org/article/2c08f73c35eb45f3b17000910fce8cbc
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spelling	oai:doaj.org-article:2c08f73c35eb45f3b17000910fce8cbc2021-12-02T15:54:13ZVisibility graph based temporal community detection with applications in biological time series10.1038/s41598-021-84838-x2045-2322https://doaj.org/article/2c08f73c35eb45f3b17000910fce8cbc2021-03-01T00:00:00Zhttps://doi.org/10.1038/s41598-021-84838-xhttps://doaj.org/toc/2045-2322Abstract Temporal behavior is an essential aspect of all biological systems. Time series have been previously represented as networks. Such representations must address two fundamental problems on how to: (1) Create appropriate networks to reflect the characteristics of biological time series. (2) Detect characteristic dynamic patterns or events as network temporal communities. General community detection methods use metrics comparing the connectivity within a community to random models, or are based on the betweenness centrality of edges or nodes. However, such methods were not designed for network representations of time series. We introduce a visibility-graph-based method to build networks from time series and detect temporal communities within these networks. To characterize unevenly sampled time series (typical of biological experiments), and simultaneously capture events associated to peaks and troughs, we introduce the Weighted Dual-Perspective Visibility Graph (WDPVG). To detect temporal communities in individual signals, we first find the shortest path of the network between start and end nodes, identifying high intensity nodes as the main stem of our community detection algorithm that act as hubs for each community. Then, we aggregate nodes outside the shortest path to the closest nodes found on the main stem based on the closest path length, thereby assigning every node to a temporal community based on proximity to the stem nodes/hubs. We demonstrate the validity and effectiveness of our method through simulation and biological applications.Minzhang ZhengSergii DomanskyiCarlo PiermarocchiGeorge I. MiasNature PortfolioarticleMedicineRScienceQENScientific Reports, Vol 11, Iss 1, Pp 1-12 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	Medicine R Science Q
spellingShingle	Medicine R Science Q Minzhang Zheng Sergii Domanskyi Carlo Piermarocchi George I. Mias Visibility graph based temporal community detection with applications in biological time series
description	Abstract Temporal behavior is an essential aspect of all biological systems. Time series have been previously represented as networks. Such representations must address two fundamental problems on how to: (1) Create appropriate networks to reflect the characteristics of biological time series. (2) Detect characteristic dynamic patterns or events as network temporal communities. General community detection methods use metrics comparing the connectivity within a community to random models, or are based on the betweenness centrality of edges or nodes. However, such methods were not designed for network representations of time series. We introduce a visibility-graph-based method to build networks from time series and detect temporal communities within these networks. To characterize unevenly sampled time series (typical of biological experiments), and simultaneously capture events associated to peaks and troughs, we introduce the Weighted Dual-Perspective Visibility Graph (WDPVG). To detect temporal communities in individual signals, we first find the shortest path of the network between start and end nodes, identifying high intensity nodes as the main stem of our community detection algorithm that act as hubs for each community. Then, we aggregate nodes outside the shortest path to the closest nodes found on the main stem based on the closest path length, thereby assigning every node to a temporal community based on proximity to the stem nodes/hubs. We demonstrate the validity and effectiveness of our method through simulation and biological applications.
format	article
author	Minzhang Zheng Sergii Domanskyi Carlo Piermarocchi George I. Mias
author_facet	Minzhang Zheng Sergii Domanskyi Carlo Piermarocchi George I. Mias
author_sort	Minzhang Zheng
title	Visibility graph based temporal community detection with applications in biological time series
title_short	Visibility graph based temporal community detection with applications in biological time series
title_full	Visibility graph based temporal community detection with applications in biological time series
title_fullStr	Visibility graph based temporal community detection with applications in biological time series
title_full_unstemmed	Visibility graph based temporal community detection with applications in biological time series
title_sort	visibility graph based temporal community detection with applications in biological time series
publisher	Nature Portfolio
publishDate	2021
url	https://doaj.org/article/2c08f73c35eb45f3b17000910fce8cbc
work_keys_str_mv	AT minzhangzheng visibilitygraphbasedtemporalcommunitydetectionwithapplicationsinbiologicaltimeseries AT sergiidomanskyi visibilitygraphbasedtemporalcommunitydetectionwithapplicationsinbiologicaltimeseries AT carlopiermarocchi visibilitygraphbasedtemporalcommunitydetectionwithapplicationsinbiologicaltimeseries AT georgeimias visibilitygraphbasedtemporalcommunitydetectionwithapplicationsinbiologicaltimeseries
_version_	1718385439214141440

Visibility graph based temporal community detection with applications in biological time series

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