Optimizing Functional Network Representation of Multivariate Time Series

By combining complex network theory and data mining techniques, we provide objective criteria for optimization of the functional network representation of generic multivariate time series. In particular, we propose a method for the principled selection of the threshold value for functional network r...

Full description

Bibliographic Details
Main Authors:	Zanin, Massimiliano, Sousa, Pedro, Papo, David, Bajo, Ricardo, García-Prieto, Juan, Pozo, Francisco del, Menasalvas, Ernestina, Boccaletti, Stefano
Format:	Online
Language:	English
Published:	Nature Publishing Group 2012
Online Access:	https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3433690/

id	pubmed-3433690
recordtype	oai_dc
spelling	pubmed-34336902012-09-05 Optimizing Functional Network Representation of Multivariate Time Series Zanin, Massimiliano Sousa, Pedro Papo, David Bajo, Ricardo García-Prieto, Juan Pozo, Francisco del Menasalvas, Ernestina Boccaletti, Stefano Article By combining complex network theory and data mining techniques, we provide objective criteria for optimization of the functional network representation of generic multivariate time series. In particular, we propose a method for the principled selection of the threshold value for functional network reconstruction from raw data, and for proper identification of the network's indicators that unveil the most discriminative information on the system for classification purposes. We illustrate our method by analysing networks of functional brain activity of healthy subjects, and patients suffering from Mild Cognitive Impairment, an intermediate stage between the expected cognitive decline of normal aging and the more pronounced decline of dementia. We discuss extensions of the scope of the proposed methodology to network engineering purposes, and to other data mining tasks. Nature Publishing Group 2012-09-05 /pmc/articles/PMC3433690/ /pubmed/22953051 http://dx.doi.org/10.1038/srep00630 Text en Copyright © 2012, Macmillan Publishers Limited. All rights reserved http://creativecommons.org/licenses/by-nc-sa/3.0/ This work is licensed under a Creative Commons Attribution-NonCommercial-ShareALike 3.0 Unported License. To view a copy of this license, visit http://creativecommons.org/licenses/by-nc-sa/3.0/
repository_type	Open Access Journal
institution_category	Foreign Institution
institution	US National Center for Biotechnology Information
building	NCBI PubMed
collection	Online Access
language	English
format	Online
author	Zanin, Massimiliano Sousa, Pedro Papo, David Bajo, Ricardo García-Prieto, Juan Pozo, Francisco del Menasalvas, Ernestina Boccaletti, Stefano
spellingShingle	Zanin, Massimiliano Sousa, Pedro Papo, David Bajo, Ricardo García-Prieto, Juan Pozo, Francisco del Menasalvas, Ernestina Boccaletti, Stefano Optimizing Functional Network Representation of Multivariate Time Series
author_facet	Zanin, Massimiliano Sousa, Pedro Papo, David Bajo, Ricardo García-Prieto, Juan Pozo, Francisco del Menasalvas, Ernestina Boccaletti, Stefano
author_sort	Zanin, Massimiliano
title	Optimizing Functional Network Representation of Multivariate Time Series
title_short	Optimizing Functional Network Representation of Multivariate Time Series
title_full	Optimizing Functional Network Representation of Multivariate Time Series
title_fullStr	Optimizing Functional Network Representation of Multivariate Time Series
title_full_unstemmed	Optimizing Functional Network Representation of Multivariate Time Series
title_sort	optimizing functional network representation of multivariate time series
description	By combining complex network theory and data mining techniques, we provide objective criteria for optimization of the functional network representation of generic multivariate time series. In particular, we propose a method for the principled selection of the threshold value for functional network reconstruction from raw data, and for proper identification of the network's indicators that unveil the most discriminative information on the system for classification purposes. We illustrate our method by analysing networks of functional brain activity of healthy subjects, and patients suffering from Mild Cognitive Impairment, an intermediate stage between the expected cognitive decline of normal aging and the more pronounced decline of dementia. We discuss extensions of the scope of the proposed methodology to network engineering purposes, and to other data mining tasks.
publisher	Nature Publishing Group
publishDate	2012
url	https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3433690/
_version_	1611554165637513216

Optimizing Functional Network Representation of Multivariate Time Series

Similar Items