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
- Institución
- Consejo Nacional de Investigaciones Científicas y Técnicas
- OAI Identificador
- oai:ri.conicet.gov.ar:11336/72880
Ver los metadatos del registro completo
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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 Técnicas2025-09-29T09:37:47Zoai: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:34982025-09-29 09:37:48.106CONICET 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 |
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1844613191980548096 |
score |
13.070432 |