Nonparametric statistics of dynamic networks with distinguishable nodes

Autores
Fraiman Borrazás, Daniel Edmundo; Fraiman, Nicolas; Fraiman, Ricardo
Año de publicación
2017
Idioma
inglés
Tipo de recurso
artículo
Estado
versión publicada
Descripción
The study of random graphs and networks had an explosive development in the last couple of decades. Meanwhile, techniques for the statistical analysis of sequences of networks were less developed. In this paper, we focus on networks sequences with a fixed number of labeled nodes and study some statistical problems in a nonparametric framework. We introduce natural notions of center and a depth function for networks that evolve in time. We develop several statistical techniques including testing, supervised and unsupervised classification, and some notions of principal component sets in the space of networks. Some examples and asymptotic results are given, as well as two real data examples.
Fil: Fraiman Borrazás, Daniel Edmundo. Universidad de San Andrés. Departamento de Matemáticas y Ciencias; Argentina. Consejo Nacional de Investigaciones Científicas y Técnicas; Argentina
Fil: Fraiman, Nicolas. University of North Carolina; Estados Unidos
Fil: Fraiman, Ricardo. Universidad de la República; Uruguay
Materia
Cluster Analysis of Graphs
Depth
Graph Estimation
Principal Components
Nivel de accesibilidad
acceso abierto
Condiciones de uso
https://creativecommons.org/licenses/by-nc-sa/2.5/ar/
Repositorio
CONICET Digital (CONICET)
Institución
Consejo Nacional de Investigaciones Científicas y Técnicas
OAI Identificador
oai:ri.conicet.gov.ar:11336/72880
Acceder
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oai_identifier_str oai:ri.conicet.gov.ar:11336/72880
network_acronym_str CONICETDig
repository_id_str 3498
network_name_str CONICET Digital (CONICET)
spelling Nonparametric statistics of dynamic networks with distinguishable nodesFraiman Borrazás, Daniel EdmundoFraiman, NicolasFraiman, RicardoCluster Analysis of GraphsDepthGraph EstimationPrincipal Componentshttps://purl.org/becyt/ford/1.1https://purl.org/becyt/ford/1The study of random graphs and networks had an explosive development in the last couple of decades. Meanwhile, techniques for the statistical analysis of sequences of networks were less developed. In this paper, we focus on networks sequences with a fixed number of labeled nodes and study some statistical problems in a nonparametric framework. We introduce natural notions of center and a depth function for networks that evolve in time. We develop several statistical techniques including testing, supervised and unsupervised classification, and some notions of principal component sets in the space of networks. Some examples and asymptotic results are given, as well as two real data examples.Fil: Fraiman Borrazás, Daniel Edmundo. Universidad de San Andrés. Departamento de Matemáticas y Ciencias; Argentina. Consejo Nacional de Investigaciones Científicas y Técnicas; ArgentinaFil: Fraiman, Nicolas. University of North Carolina; Estados UnidosFil: Fraiman, Ricardo. Universidad de la República; UruguaySpringer2017-09info:eu-repo/semantics/articleinfo:eu-repo/semantics/publishedVersionhttp://purl.org/coar/resource_type/c_6501info:ar-repo/semantics/articuloapplication/pdfapplication/zipapplication/pdfhttp://hdl.handle.net/11336/72880Fraiman Borrazás, Daniel Edmundo; Fraiman, Nicolas; Fraiman, Ricardo; Nonparametric statistics of dynamic networks with distinguishable nodes; Springer; Test; 26; 3; 9-2017; 546-5731133-06861863-8260CONICET DigitalCONICETenginfo:eu-repo/semantics/altIdentifier/url/https://link.springer.com/article/10.1007/s11749-017-0524-8info:eu-repo/semantics/altIdentifier/doi/10.1007/s11749-017-0524-8info:eu-repo/semantics/openAccesshttps://creativecommons.org/licenses/by-nc-sa/2.5/ar/reponame:CONICET Digital (CONICET)instname:Consejo Nacional de Investigaciones Científicas y Técnicas2025-10-22T11:05:58Zoai:ri.conicet.gov.ar:11336/72880instacron:CONICETInstitucionalhttp://ri.conicet.gov.ar/Organismo científico-tecnológicoNo correspondehttp://ri.conicet.gov.ar/oai/requestdasensio@conicet.gov.ar; lcarlino@conicet.gov.arArgentinaNo correspondeNo correspondeNo correspondeopendoar:34982025-10-22 11:05:58.609CONICET Digital (CONICET) - Consejo Nacional de Investigaciones Científicas y Técnicasfalse
dc.title.none.fl_str_mv Nonparametric statistics of dynamic networks with distinguishable nodes
title Nonparametric statistics of dynamic networks with distinguishable nodes
spellingShingle Nonparametric statistics of dynamic networks with distinguishable nodes
Fraiman Borrazás, Daniel Edmundo
Cluster Analysis of Graphs
Depth
Graph Estimation
Principal Components
title_short Nonparametric statistics of dynamic networks with distinguishable nodes
title_full Nonparametric statistics of dynamic networks with distinguishable nodes
title_fullStr Nonparametric statistics of dynamic networks with distinguishable nodes
title_full_unstemmed Nonparametric statistics of dynamic networks with distinguishable nodes
title_sort Nonparametric statistics of dynamic networks with distinguishable nodes
dc.creator.none.fl_str_mv Fraiman Borrazás, Daniel Edmundo
Fraiman, Nicolas
Fraiman, Ricardo
author Fraiman Borrazás, Daniel Edmundo
author_facet Fraiman Borrazás, Daniel Edmundo
Fraiman, Nicolas
Fraiman, Ricardo
author_role author
author2 Fraiman, Nicolas
Fraiman, Ricardo
author2_role author
author
dc.subject.none.fl_str_mv Cluster Analysis of Graphs
Depth
Graph Estimation
Principal Components
topic Cluster Analysis of Graphs
Depth
Graph Estimation
Principal Components
purl_subject.fl_str_mv https://purl.org/becyt/ford/1.1
https://purl.org/becyt/ford/1
dc.description.none.fl_txt_mv The study of random graphs and networks had an explosive development in the last couple of decades. Meanwhile, techniques for the statistical analysis of sequences of networks were less developed. In this paper, we focus on networks sequences with a fixed number of labeled nodes and study some statistical problems in a nonparametric framework. We introduce natural notions of center and a depth function for networks that evolve in time. We develop several statistical techniques including testing, supervised and unsupervised classification, and some notions of principal component sets in the space of networks. Some examples and asymptotic results are given, as well as two real data examples.
Fil: Fraiman Borrazás, Daniel Edmundo. Universidad de San Andrés. Departamento de Matemáticas y Ciencias; Argentina. Consejo Nacional de Investigaciones Científicas y Técnicas; Argentina
Fil: Fraiman, Nicolas. University of North Carolina; Estados Unidos
Fil: Fraiman, Ricardo. Universidad de la República; Uruguay
description The study of random graphs and networks had an explosive development in the last couple of decades. Meanwhile, techniques for the statistical analysis of sequences of networks were less developed. In this paper, we focus on networks sequences with a fixed number of labeled nodes and study some statistical problems in a nonparametric framework. We introduce natural notions of center and a depth function for networks that evolve in time. We develop several statistical techniques including testing, supervised and unsupervised classification, and some notions of principal component sets in the space of networks. Some examples and asymptotic results are given, as well as two real data examples.
publishDate 2017
dc.date.none.fl_str_mv 2017-09
dc.type.none.fl_str_mv info:eu-repo/semantics/article
info:eu-repo/semantics/publishedVersion
http://purl.org/coar/resource_type/c_6501
info:ar-repo/semantics/articulo
format article
status_str publishedVersion
dc.identifier.none.fl_str_mv http://hdl.handle.net/11336/72880
Fraiman Borrazás, Daniel Edmundo; Fraiman, Nicolas; Fraiman, Ricardo; Nonparametric statistics of dynamic networks with distinguishable nodes; Springer; Test; 26; 3; 9-2017; 546-573
1133-0686
1863-8260
CONICET Digital
CONICET
url http://hdl.handle.net/11336/72880
identifier_str_mv Fraiman Borrazás, Daniel Edmundo; Fraiman, Nicolas; Fraiman, Ricardo; Nonparametric statistics of dynamic networks with distinguishable nodes; Springer; Test; 26; 3; 9-2017; 546-573
1133-0686
1863-8260
CONICET Digital
CONICET
dc.language.none.fl_str_mv eng
language eng
dc.relation.none.fl_str_mv info:eu-repo/semantics/altIdentifier/url/https://link.springer.com/article/10.1007/s11749-017-0524-8
info:eu-repo/semantics/altIdentifier/doi/10.1007/s11749-017-0524-8
dc.rights.none.fl_str_mv info:eu-repo/semantics/openAccess
https://creativecommons.org/licenses/by-nc-sa/2.5/ar/
eu_rights_str_mv openAccess
rights_invalid_str_mv https://creativecommons.org/licenses/by-nc-sa/2.5/ar/
dc.format.none.fl_str_mv application/pdf
application/zip
application/pdf
dc.publisher.none.fl_str_mv Springer
publisher.none.fl_str_mv Springer
dc.source.none.fl_str_mv reponame:CONICET Digital (CONICET)
instname:Consejo Nacional de Investigaciones Científicas y Técnicas
reponame_str CONICET Digital (CONICET)
collection CONICET Digital (CONICET)
instname_str Consejo Nacional de Investigaciones Científicas y Técnicas
repository.name.fl_str_mv CONICET Digital (CONICET) - Consejo Nacional de Investigaciones Científicas y Técnicas
repository.mail.fl_str_mv dasensio@conicet.gov.ar; lcarlino@conicet.gov.ar
_version_ 1846781349707382784
score 12.982451

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