Learning attribute and homophily measures through random walks
Autor(a) principal: | |
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Data de Publicação: | 2023 |
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/10400.1/19871 |
Resumo: | We investigate the statistical learning of nodal attribute functionals in homophily networks using random walks. Attributes can be discrete or continuous. A generalization of various existing canonical models, based on preferential attachment is studied (model class P), where new nodes form connections dependent on both their attribute values and popularity as measured by degree. An associated model class U is described, which is amenable to theoretical analysis and gives access to asymptotics of a host of functionals of interest. Settings where asymptotics for model class U transfer over to model class P through the phenomenon of resolvability are analyzed. For the statistical learning, we consider several canonical attribute agnostic sampling schemes such as Metropolis-Hasting random walk, versions of node2vec (Grover and Leskovec, 2016) that incorporate both classical random walk and non-backtracking propensities and propose new variants which use attribute information in addition to topological information to explore the network. Estimators for learning the attribute distribution, degree distribution for an attribute type and homophily measures are proposed. The performance of such statistical learning framework is studied on both synthetic networks (model class P) and real world systems, and its dependence on the network topology, degree of homophily or absence thereof, (un)balanced attributes, is assessed. |
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Learning attribute and homophily measures through random walksAttributed networksHomophilyNetwork modelResolvabilityRandom walk samplingsDiscrete and continuous attributesLearning attribute functionalsWe investigate the statistical learning of nodal attribute functionals in homophily networks using random walks. Attributes can be discrete or continuous. A generalization of various existing canonical models, based on preferential attachment is studied (model class P), where new nodes form connections dependent on both their attribute values and popularity as measured by degree. An associated model class U is described, which is amenable to theoretical analysis and gives access to asymptotics of a host of functionals of interest. Settings where asymptotics for model class U transfer over to model class P through the phenomenon of resolvability are analyzed. For the statistical learning, we consider several canonical attribute agnostic sampling schemes such as Metropolis-Hasting random walk, versions of node2vec (Grover and Leskovec, 2016) that incorporate both classical random walk and non-backtracking propensities and propose new variants which use attribute information in addition to topological information to explore the network. Estimators for learning the attribute distribution, degree distribution for an attribute type and homophily measures are proposed. The performance of such statistical learning framework is studied on both synthetic networks (model class P) and real world systems, and its dependence on the network topology, degree of homophily or absence thereof, (un)balanced attributes, is assessed.S. Banerjee is partially supported by the NSF CAREER award DMS-2141621. S. Bhamidi and V. Pipiras are partially supported by NSF DMS-2113662. S. Banerjee, S. Bhamidi and V.Pipiras are partially supported by NSF RTG grant DMS-2134107Springer OpenSapientiaAntunes, NelsonBanerjee, SayanBhamidi, ShankarPipiras, Vladas2023-07-26T11:13:18Z2023-06-272023-07-01T03:28:49Z2023-06-27T00:00:00Zinfo:eu-repo/semantics/publishedVersioninfo:eu-repo/semantics/articleapplication/pdfhttp://hdl.handle.net/10400.1/19871engenApplied Network Science. 2023 Jun 27;8(1):3910.1007/s41109-023-00558-3info: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:RCAAP2023-08-02T02:02:01Zoai:sapientia.ualg.pt:10400.1/19871Portal AgregadorONGhttps://www.rcaap.pt/oai/openaireopendoar:71602024-03-19T20:10:25.255169Repositório 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 |
Learning attribute and homophily measures through random walks |
title |
Learning attribute and homophily measures through random walks |
spellingShingle |
Learning attribute and homophily measures through random walks Antunes, Nelson Attributed networks Homophily Network model Resolvability Random walk samplings Discrete and continuous attributes Learning attribute functionals |
title_short |
Learning attribute and homophily measures through random walks |
title_full |
Learning attribute and homophily measures through random walks |
title_fullStr |
Learning attribute and homophily measures through random walks |
title_full_unstemmed |
Learning attribute and homophily measures through random walks |
title_sort |
Learning attribute and homophily measures through random walks |
author |
Antunes, Nelson |
author_facet |
Antunes, Nelson Banerjee, Sayan Bhamidi, Shankar Pipiras, Vladas |
author_role |
author |
author2 |
Banerjee, Sayan Bhamidi, Shankar Pipiras, Vladas |
author2_role |
author author author |
dc.contributor.none.fl_str_mv |
Sapientia |
dc.contributor.author.fl_str_mv |
Antunes, Nelson Banerjee, Sayan Bhamidi, Shankar Pipiras, Vladas |
dc.subject.por.fl_str_mv |
Attributed networks Homophily Network model Resolvability Random walk samplings Discrete and continuous attributes Learning attribute functionals |
topic |
Attributed networks Homophily Network model Resolvability Random walk samplings Discrete and continuous attributes Learning attribute functionals |
description |
We investigate the statistical learning of nodal attribute functionals in homophily networks using random walks. Attributes can be discrete or continuous. A generalization of various existing canonical models, based on preferential attachment is studied (model class P), where new nodes form connections dependent on both their attribute values and popularity as measured by degree. An associated model class U is described, which is amenable to theoretical analysis and gives access to asymptotics of a host of functionals of interest. Settings where asymptotics for model class U transfer over to model class P through the phenomenon of resolvability are analyzed. For the statistical learning, we consider several canonical attribute agnostic sampling schemes such as Metropolis-Hasting random walk, versions of node2vec (Grover and Leskovec, 2016) that incorporate both classical random walk and non-backtracking propensities and propose new variants which use attribute information in addition to topological information to explore the network. Estimators for learning the attribute distribution, degree distribution for an attribute type and homophily measures are proposed. The performance of such statistical learning framework is studied on both synthetic networks (model class P) and real world systems, and its dependence on the network topology, degree of homophily or absence thereof, (un)balanced attributes, is assessed. |
publishDate |
2023 |
dc.date.none.fl_str_mv |
2023-07-26T11:13:18Z 2023-06-27 2023-07-01T03:28:49Z 2023-06-27T00:00:00Z |
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/10400.1/19871 |
url |
http://hdl.handle.net/10400.1/19871 |
dc.language.iso.fl_str_mv |
eng en |
language |
eng |
language_invalid_str_mv |
en |
dc.relation.none.fl_str_mv |
Applied Network Science. 2023 Jun 27;8(1):39 10.1007/s41109-023-00558-3 |
dc.rights.driver.fl_str_mv |
info:eu-repo/semantics/openAccess |
eu_rights_str_mv |
openAccess |
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application/pdf |
dc.publisher.none.fl_str_mv |
Springer Open |
publisher.none.fl_str_mv |
Springer Open |
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Agência para a Sociedade do Conhecimento (UMIC) - FCT - Sociedade da Informação |
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RCAAP |
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RCAAP |
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Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos) |
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Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos) |
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Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos) - Agência para a Sociedade do Conhecimento (UMIC) - FCT - Sociedade da Informação |
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