Sampling methods and estimation of triangle count distributions in large networks
Autor(a) principal: | |
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Data de Publicação: | 2021 |
Outros Autores: | , |
Tipo de documento: | Artigo |
Idioma: | eng |
Título da fonte: | Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos) |
Texto Completo: | http://hdl.handle.net/10400.1/17160 |
Resumo: | This paper investigates the distributions of triangle counts per vertex and edge, as a means for network description, analysis, model building, and other tasks. The main interest is in estimating these distributions through sampling, especially for large networks. A novel sampling method tailored for the estimation analysis is proposed, with three sampling designs motivated by several network access scenarios. An estimation method based on inversion and an asymptotic method are developed to recover the entire distribution. A single method to estimate the distribution using multiple samples is also considered. Algorithms are presented to sample the network under the various access scenarios. Finally, the estimation methods on synthetic and real-world networks are evaluated in a data study. |
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Sampling methods and estimation of triangle count distributions in large networksTrianglesRandom samplingDistribution estimationInversion approachAsymptotic approachMultiple samplesStatic and streaming graphsPower lawsThis paper investigates the distributions of triangle counts per vertex and edge, as a means for network description, analysis, model building, and other tasks. The main interest is in estimating these distributions through sampling, especially for large networks. A novel sampling method tailored for the estimation analysis is proposed, with three sampling designs motivated by several network access scenarios. An estimation method based on inversion and an asymptotic method are developed to recover the entire distribution. A single method to estimate the distribution using multiple samples is also considered. Algorithms are presented to sample the network under the various access scenarios. Finally, the estimation methods on synthetic and real-world networks are evaluated in a data study.Cambridge University PressSapientiaAntunes, NelsonGuo, TianjianPipiras, Vladas2021-09-27T14:25:45Z2021-102021-10-01T00:00:00Zinfo:eu-repo/semantics/publishedVersioninfo:eu-repo/semantics/articleapplication/pdfhttp://hdl.handle.net/10400.1/17160eng10.1017/nws.2021.22050-1250info:eu-repo/semantics/openAccessreponame:Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos)instname:Agência para a Sociedade do Conhecimento (UMIC) - FCT - Sociedade da Informaçãoinstacron:RCAAP2023-07-24T10:29:17Zoai:sapientia.ualg.pt:10400.1/17160Portal AgregadorONGhttps://www.rcaap.pt/oai/openaireopendoar:71602024-03-19T20:07:09.100689Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos) - Agência para a Sociedade do Conhecimento (UMIC) - FCT - Sociedade da Informaçãofalse |
dc.title.none.fl_str_mv |
Sampling methods and estimation of triangle count distributions in large networks |
title |
Sampling methods and estimation of triangle count distributions in large networks |
spellingShingle |
Sampling methods and estimation of triangle count distributions in large networks Antunes, Nelson Triangles Random sampling Distribution estimation Inversion approach Asymptotic approach Multiple samples Static and streaming graphs Power laws |
title_short |
Sampling methods and estimation of triangle count distributions in large networks |
title_full |
Sampling methods and estimation of triangle count distributions in large networks |
title_fullStr |
Sampling methods and estimation of triangle count distributions in large networks |
title_full_unstemmed |
Sampling methods and estimation of triangle count distributions in large networks |
title_sort |
Sampling methods and estimation of triangle count distributions in large networks |
author |
Antunes, Nelson |
author_facet |
Antunes, Nelson Guo, Tianjian Pipiras, Vladas |
author_role |
author |
author2 |
Guo, Tianjian Pipiras, Vladas |
author2_role |
author author |
dc.contributor.none.fl_str_mv |
Sapientia |
dc.contributor.author.fl_str_mv |
Antunes, Nelson Guo, Tianjian Pipiras, Vladas |
dc.subject.por.fl_str_mv |
Triangles Random sampling Distribution estimation Inversion approach Asymptotic approach Multiple samples Static and streaming graphs Power laws |
topic |
Triangles Random sampling Distribution estimation Inversion approach Asymptotic approach Multiple samples Static and streaming graphs Power laws |
description |
This paper investigates the distributions of triangle counts per vertex and edge, as a means for network description, analysis, model building, and other tasks. The main interest is in estimating these distributions through sampling, especially for large networks. A novel sampling method tailored for the estimation analysis is proposed, with three sampling designs motivated by several network access scenarios. An estimation method based on inversion and an asymptotic method are developed to recover the entire distribution. A single method to estimate the distribution using multiple samples is also considered. Algorithms are presented to sample the network under the various access scenarios. Finally, the estimation methods on synthetic and real-world networks are evaluated in a data study. |
publishDate |
2021 |
dc.date.none.fl_str_mv |
2021-09-27T14:25:45Z 2021-10 2021-10-01T00:00:00Z |
dc.type.status.fl_str_mv |
info:eu-repo/semantics/publishedVersion |
dc.type.driver.fl_str_mv |
info:eu-repo/semantics/article |
format |
article |
status_str |
publishedVersion |
dc.identifier.uri.fl_str_mv |
http://hdl.handle.net/10400.1/17160 |
url |
http://hdl.handle.net/10400.1/17160 |
dc.language.iso.fl_str_mv |
eng |
language |
eng |
dc.relation.none.fl_str_mv |
10.1017/nws.2021.2 2050-1250 |
dc.rights.driver.fl_str_mv |
info:eu-repo/semantics/openAccess |
eu_rights_str_mv |
openAccess |
dc.format.none.fl_str_mv |
application/pdf |
dc.publisher.none.fl_str_mv |
Cambridge University Press |
publisher.none.fl_str_mv |
Cambridge University Press |
dc.source.none.fl_str_mv |
reponame:Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos) instname:Agência para a Sociedade do Conhecimento (UMIC) - FCT - Sociedade da Informação instacron:RCAAP |
instname_str |
Agência para a Sociedade do Conhecimento (UMIC) - FCT - Sociedade da Informação |
instacron_str |
RCAAP |
institution |
RCAAP |
reponame_str |
Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos) |
collection |
Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos) |
repository.name.fl_str_mv |
Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos) - Agência para a Sociedade do Conhecimento (UMIC) - FCT - Sociedade da Informação |
repository.mail.fl_str_mv |
|
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1799133315718971392 |