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Unraveling Spurious Properties of Interaction Networks with Tailored Random Networks

We investigate interaction networks that we derive from multivariate time series with methods frequently employed in diverse scientific fields such as biology, quantitative finance, physics, earth and climate sciences, and the neurosciences. Mimicking experimental situations, we generate time series...

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Detalles Bibliográficos
Autores principales: Bialonski, Stephan, Wendler, Martin, Lehnertz, Klaus
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Public Library of Science 2011
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3151270/
https://www.ncbi.nlm.nih.gov/pubmed/21850239
http://dx.doi.org/10.1371/journal.pone.0022826
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author Bialonski, Stephan
Wendler, Martin
Lehnertz, Klaus
author_facet Bialonski, Stephan
Wendler, Martin
Lehnertz, Klaus
author_sort Bialonski, Stephan
collection PubMed
description We investigate interaction networks that we derive from multivariate time series with methods frequently employed in diverse scientific fields such as biology, quantitative finance, physics, earth and climate sciences, and the neurosciences. Mimicking experimental situations, we generate time series with finite length and varying frequency content but from independent stochastic processes. Using the correlation coefficient and the maximum cross-correlation, we estimate interdependencies between these time series. With clustering coefficient and average shortest path length, we observe unweighted interaction networks, derived via thresholding the values of interdependence, to possess non-trivial topologies as compared to Erdös-Rényi networks, which would indicate small-world characteristics. These topologies reflect the mostly unavoidable finiteness of the data, which limits the reliability of typically used estimators of signal interdependence. We propose random networks that are tailored to the way interaction networks are derived from empirical data. Through an exemplary investigation of multichannel electroencephalographic recordings of epileptic seizures – known for their complex spatial and temporal dynamics – we show that such random networks help to distinguish network properties of interdependence structures related to seizure dynamics from those spuriously induced by the applied methods of analysis.
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spelling pubmed-31512702011-08-17 Unraveling Spurious Properties of Interaction Networks with Tailored Random Networks Bialonski, Stephan Wendler, Martin Lehnertz, Klaus PLoS One Research Article We investigate interaction networks that we derive from multivariate time series with methods frequently employed in diverse scientific fields such as biology, quantitative finance, physics, earth and climate sciences, and the neurosciences. Mimicking experimental situations, we generate time series with finite length and varying frequency content but from independent stochastic processes. Using the correlation coefficient and the maximum cross-correlation, we estimate interdependencies between these time series. With clustering coefficient and average shortest path length, we observe unweighted interaction networks, derived via thresholding the values of interdependence, to possess non-trivial topologies as compared to Erdös-Rényi networks, which would indicate small-world characteristics. These topologies reflect the mostly unavoidable finiteness of the data, which limits the reliability of typically used estimators of signal interdependence. We propose random networks that are tailored to the way interaction networks are derived from empirical data. Through an exemplary investigation of multichannel electroencephalographic recordings of epileptic seizures – known for their complex spatial and temporal dynamics – we show that such random networks help to distinguish network properties of interdependence structures related to seizure dynamics from those spuriously induced by the applied methods of analysis. Public Library of Science 2011-08-05 /pmc/articles/PMC3151270/ /pubmed/21850239 http://dx.doi.org/10.1371/journal.pone.0022826 Text en Bialonski et al. http://creativecommons.org/licenses/by/4.0/ This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are properly credited.
spellingShingle Research Article
Bialonski, Stephan
Wendler, Martin
Lehnertz, Klaus
Unraveling Spurious Properties of Interaction Networks with Tailored Random Networks
title Unraveling Spurious Properties of Interaction Networks with Tailored Random Networks
title_full Unraveling Spurious Properties of Interaction Networks with Tailored Random Networks
title_fullStr Unraveling Spurious Properties of Interaction Networks with Tailored Random Networks
title_full_unstemmed Unraveling Spurious Properties of Interaction Networks with Tailored Random Networks
title_short Unraveling Spurious Properties of Interaction Networks with Tailored Random Networks
title_sort unraveling spurious properties of interaction networks with tailored random networks
topic Research Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3151270/
https://www.ncbi.nlm.nih.gov/pubmed/21850239
http://dx.doi.org/10.1371/journal.pone.0022826
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