An electrostatics method for converting a time-series into a weighted complex network

Abstract This paper proposes a new method for converting a time-series into a weighted graph (complex network), which builds on electrostatics in physics. The proposed method conceptualizes a time-series as a series of stationary, electrically charged particles, on which Coulomb-like forces can be c...

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Autores principales:	Dimitrios Tsiotas, Lykourgos Magafas, Panos Argyrakis
Formato:	article
Lenguaje:	EN
Publicado:	Nature Portfolio 2021
Materias:	Medicine R Science Q
Acceso en línea:	https://doaj.org/article/124abb98aafa4a008be459192d2e0d3c
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spelling	oai:doaj.org-article:124abb98aafa4a008be459192d2e0d3c2021-12-02T17:51:13ZAn electrostatics method for converting a time-series into a weighted complex network10.1038/s41598-021-89552-22045-2322https://doaj.org/article/124abb98aafa4a008be459192d2e0d3c2021-06-01T00:00:00Zhttps://doi.org/10.1038/s41598-021-89552-2https://doaj.org/toc/2045-2322Abstract This paper proposes a new method for converting a time-series into a weighted graph (complex network), which builds on electrostatics in physics. The proposed method conceptualizes a time-series as a series of stationary, electrically charged particles, on which Coulomb-like forces can be computed. This allows generating electrostatic-like graphs associated with time-series that, additionally to the existing transformations, can be also weighted and sometimes disconnected. Within this context, this paper examines the structural similarity between five different types of time-series and their associated graphs that are generated by the proposed algorithm and the visibility graph, which is currently the most popular algorithm in the literature. The analysis compares the source (original) time-series with the node-series generated by network measures (that are arranged into the node-ordering of the source time-series), in terms of a linear trend, chaotic behaviour, stationarity, periodicity, and cyclical structure. It is shown that the proposed electrostatic graph algorithm generates graphs with node-measures that are more representative of the structure of the source time-series than the visibility graph. This makes the proposed algorithm more natural rather than algebraic, in comparison with existing physics-defined methods. The overall approach also suggests a methodological framework for evaluating the structural relevance between the source time-series and their associated graphs produced by any possible transformation.Dimitrios TsiotasLykourgos MagafasPanos ArgyrakisNature PortfolioarticleMedicineRScienceQENScientific Reports, Vol 11, Iss 1, Pp 1-15 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	Medicine R Science Q
spellingShingle	Medicine R Science Q Dimitrios Tsiotas Lykourgos Magafas Panos Argyrakis An electrostatics method for converting a time-series into a weighted complex network
description	Abstract This paper proposes a new method for converting a time-series into a weighted graph (complex network), which builds on electrostatics in physics. The proposed method conceptualizes a time-series as a series of stationary, electrically charged particles, on which Coulomb-like forces can be computed. This allows generating electrostatic-like graphs associated with time-series that, additionally to the existing transformations, can be also weighted and sometimes disconnected. Within this context, this paper examines the structural similarity between five different types of time-series and their associated graphs that are generated by the proposed algorithm and the visibility graph, which is currently the most popular algorithm in the literature. The analysis compares the source (original) time-series with the node-series generated by network measures (that are arranged into the node-ordering of the source time-series), in terms of a linear trend, chaotic behaviour, stationarity, periodicity, and cyclical structure. It is shown that the proposed electrostatic graph algorithm generates graphs with node-measures that are more representative of the structure of the source time-series than the visibility graph. This makes the proposed algorithm more natural rather than algebraic, in comparison with existing physics-defined methods. The overall approach also suggests a methodological framework for evaluating the structural relevance between the source time-series and their associated graphs produced by any possible transformation.
format	article
author	Dimitrios Tsiotas Lykourgos Magafas Panos Argyrakis
author_facet	Dimitrios Tsiotas Lykourgos Magafas Panos Argyrakis
author_sort	Dimitrios Tsiotas
title	An electrostatics method for converting a time-series into a weighted complex network
title_short	An electrostatics method for converting a time-series into a weighted complex network
title_full	An electrostatics method for converting a time-series into a weighted complex network
title_fullStr	An electrostatics method for converting a time-series into a weighted complex network
title_full_unstemmed	An electrostatics method for converting a time-series into a weighted complex network
title_sort	electrostatics method for converting a time-series into a weighted complex network
publisher	Nature Portfolio
publishDate	2021
url	https://doaj.org/article/124abb98aafa4a008be459192d2e0d3c
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An electrostatics method for converting a time-series into a weighted complex network

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