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Change Point Detection in Correlation Networks

Many systems of interacting elements can be conceptualized as networks, where network nodes represent the elements and network ties represent interactions between the elements. In systems where the underlying network evolves, it is useful to determine the points in time where the network structure c...

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Detalles Bibliográficos
Autores principales: Barnett, Ian, Onnela, Jukka-Pekka
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Nature Publishing Group 2016
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4703970/
https://www.ncbi.nlm.nih.gov/pubmed/26739105
http://dx.doi.org/10.1038/srep18893
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author Barnett, Ian
Onnela, Jukka-Pekka
author_facet Barnett, Ian
Onnela, Jukka-Pekka
author_sort Barnett, Ian
collection PubMed
description Many systems of interacting elements can be conceptualized as networks, where network nodes represent the elements and network ties represent interactions between the elements. In systems where the underlying network evolves, it is useful to determine the points in time where the network structure changes significantly as these may correspond to functional change points. We propose a method for detecting change points in correlation networks that, unlike previous change point detection methods designed for time series data, requires minimal distributional assumptions. We investigate the difficulty of change point detection near the boundaries of the time series in correlation networks and study the power of our method and competing methods through simulation. We also show the generalizable nature of the method by applying it to stock price data as well as fMRI data.
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spelling pubmed-47039702016-01-19 Change Point Detection in Correlation Networks Barnett, Ian Onnela, Jukka-Pekka Sci Rep Article Many systems of interacting elements can be conceptualized as networks, where network nodes represent the elements and network ties represent interactions between the elements. In systems where the underlying network evolves, it is useful to determine the points in time where the network structure changes significantly as these may correspond to functional change points. We propose a method for detecting change points in correlation networks that, unlike previous change point detection methods designed for time series data, requires minimal distributional assumptions. We investigate the difficulty of change point detection near the boundaries of the time series in correlation networks and study the power of our method and competing methods through simulation. We also show the generalizable nature of the method by applying it to stock price data as well as fMRI data. Nature Publishing Group 2016-01-07 /pmc/articles/PMC4703970/ /pubmed/26739105 http://dx.doi.org/10.1038/srep18893 Text en Copyright © 2016, Macmillan Publishers Limited http://creativecommons.org/licenses/by/4.0/ This work is licensed under a Creative Commons Attribution 4.0 International License. The images or other third party material in this article are included in the article’s Creative Commons license, unless indicated otherwise in the credit line; if the material is not included under the Creative Commons license, users will need to obtain permission from the license holder to reproduce the material. To view a copy of this license, visit http://creativecommons.org/licenses/by/4.0/
spellingShingle Article
Barnett, Ian
Onnela, Jukka-Pekka
Change Point Detection in Correlation Networks
title Change Point Detection in Correlation Networks
title_full Change Point Detection in Correlation Networks
title_fullStr Change Point Detection in Correlation Networks
title_full_unstemmed Change Point Detection in Correlation Networks
title_short Change Point Detection in Correlation Networks
title_sort change point detection in correlation networks
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4703970/
https://www.ncbi.nlm.nih.gov/pubmed/26739105
http://dx.doi.org/10.1038/srep18893
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