Detecting the community structure and activity patterns of temporal networks: a non-negative tensor factorization approach.

The increasing availability of temporal network data is calling for more research on extracting and characterizing mesoscopic structures in temporal networks and on relating such structure to specific functions or properties of the system. An outstanding challenge is the extension of the results ach...

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Autores principales:	Laetitia Gauvin, André Panisson, Ciro Cattuto
Formato:	article
Lenguaje:	EN
Publicado:	Public Library of Science (PLoS) 2014
Materias:	Medicine R Science Q
Acceso en línea:	https://doaj.org/article/8619e92618704155a9919c5379c3ac4b
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spelling	oai:doaj.org-article:8619e92618704155a9919c5379c3ac4b2021-11-18T08:34:42ZDetecting the community structure and activity patterns of temporal networks: a non-negative tensor factorization approach.1932-620310.1371/journal.pone.0086028https://doaj.org/article/8619e92618704155a9919c5379c3ac4b2014-01-01T00:00:00Zhttps://www.ncbi.nlm.nih.gov/pmc/articles/pmid/24497935/?tool=EBIhttps://doaj.org/toc/1932-6203The increasing availability of temporal network data is calling for more research on extracting and characterizing mesoscopic structures in temporal networks and on relating such structure to specific functions or properties of the system. An outstanding challenge is the extension of the results achieved for static networks to time-varying networks, where the topological structure of the system and the temporal activity patterns of its components are intertwined. Here we investigate the use of a latent factor decomposition technique, non-negative tensor factorization, to extract the community-activity structure of temporal networks. The method is intrinsically temporal and allows to simultaneously identify communities and to track their activity over time. We represent the time-varying adjacency matrix of a temporal network as a three-way tensor and approximate this tensor as a sum of terms that can be interpreted as communities of nodes with an associated activity time series. We summarize known computational techniques for tensor decomposition and discuss some quality metrics that can be used to tune the complexity of the factorized representation. We subsequently apply tensor factorization to a temporal network for which a ground truth is available for both the community structure and the temporal activity patterns. The data we use describe the social interactions of students in a school, the associations between students and school classes, and the spatio-temporal trajectories of students over time. We show that non-negative tensor factorization is capable of recovering the class structure with high accuracy. In particular, the extracted tensor components can be validated either as known school classes, or in terms of correlated activity patterns, i.e., of spatial and temporal coincidences that are determined by the known school activity schedule.Laetitia GauvinAndré PanissonCiro CattutoPublic Library of Science (PLoS)articleMedicineRScienceQENPLoS ONE, Vol 9, Iss 1, p e86028 (2014)
institution	DOAJ
collection	DOAJ
language	EN
topic	Medicine R Science Q
spellingShingle	Medicine R Science Q Laetitia Gauvin André Panisson Ciro Cattuto Detecting the community structure and activity patterns of temporal networks: a non-negative tensor factorization approach.
description	The increasing availability of temporal network data is calling for more research on extracting and characterizing mesoscopic structures in temporal networks and on relating such structure to specific functions or properties of the system. An outstanding challenge is the extension of the results achieved for static networks to time-varying networks, where the topological structure of the system and the temporal activity patterns of its components are intertwined. Here we investigate the use of a latent factor decomposition technique, non-negative tensor factorization, to extract the community-activity structure of temporal networks. The method is intrinsically temporal and allows to simultaneously identify communities and to track their activity over time. We represent the time-varying adjacency matrix of a temporal network as a three-way tensor and approximate this tensor as a sum of terms that can be interpreted as communities of nodes with an associated activity time series. We summarize known computational techniques for tensor decomposition and discuss some quality metrics that can be used to tune the complexity of the factorized representation. We subsequently apply tensor factorization to a temporal network for which a ground truth is available for both the community structure and the temporal activity patterns. The data we use describe the social interactions of students in a school, the associations between students and school classes, and the spatio-temporal trajectories of students over time. We show that non-negative tensor factorization is capable of recovering the class structure with high accuracy. In particular, the extracted tensor components can be validated either as known school classes, or in terms of correlated activity patterns, i.e., of spatial and temporal coincidences that are determined by the known school activity schedule.
format	article
author	Laetitia Gauvin André Panisson Ciro Cattuto
author_facet	Laetitia Gauvin André Panisson Ciro Cattuto
author_sort	Laetitia Gauvin
title	Detecting the community structure and activity patterns of temporal networks: a non-negative tensor factorization approach.
title_short	Detecting the community structure and activity patterns of temporal networks: a non-negative tensor factorization approach.
title_full	Detecting the community structure and activity patterns of temporal networks: a non-negative tensor factorization approach.
title_fullStr	Detecting the community structure and activity patterns of temporal networks: a non-negative tensor factorization approach.
title_full_unstemmed	Detecting the community structure and activity patterns of temporal networks: a non-negative tensor factorization approach.
title_sort	detecting the community structure and activity patterns of temporal networks: a non-negative tensor factorization approach.
publisher	Public Library of Science (PLoS)
publishDate	2014
url	https://doaj.org/article/8619e92618704155a9919c5379c3ac4b
work_keys_str_mv	AT laetitiagauvin detectingthecommunitystructureandactivitypatternsoftemporalnetworksanonnegativetensorfactorizationapproach AT andrepanisson detectingthecommunitystructureandactivitypatternsoftemporalnetworksanonnegativetensorfactorizationapproach AT cirocattuto detectingthecommunitystructureandactivitypatternsoftemporalnetworksanonnegativetensorfactorizationapproach
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Detecting the community structure and activity patterns of temporal networks: a non-negative tensor factorization approach.

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