Inference of Demographic Attributes based on Mobile Phone Usage Patterns and Social Network Topology

Autores
Sarraute, Carlos; Brea, Jorge; Burroni, Javier; Blanc, Pablo
Año de publicación
2015
Idioma
inglés
Tipo de recurso
artículo
Estado
versión publicada
Descripción
Mobile phone usage provides a wealth of information, which can be used to better understand the demographic structure of a population. In this paper, we focus on the population of Mexican mobile phone users. We first present an observational study of mobile phone usage according to gender and age groups. We are able to detect significant differences in phone usage among different subgroups of the population. We then study the performance of different machine learning (ML) methods to predict demographic features (namely, age and gender) of unlabeled users by leveraging individual calling patterns, as well as the structure of the communication graph. We show how a specific implementation of a diffusion model, harnessing the graph structure, has significantly better performance over other node-based standard ML methods. We provide details of the methodology together with an analysis of the robustness of our results to changes in the model parameters. Furthermore, by carefully examining the topological relations of the training nodes (seed nodes) to the rest of the nodes in the network, we find topological metrics which have a direct influence on the performance of the algorithm.
Fil: Sarraute, Carlos. Grandata Labs; Argentina
Fil: Brea, Jorge. Grandata Labs; Argentina
Fil: Burroni, Javier. Grandata Labs; Argentina
Fil: Blanc, Pablo. Consejo Nacional de Investigaciones Científicas y Técnicas. Oficina de Coordinación Administrativa Ciudad Universitaria. Instituto de Investigaciones Matemáticas "Luis A. Santalo". Universidad de Buenos Aires. Facultad de Ciencias Exactas y Naturales. Instituto de Investigaciones Matemáticas "Luis A. Santalo"; Argentina
Materia
Social Network Analysis
Mobile Phone Social Network
Call Detail Records
Graph Mining
Demographics
Homophily
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:11336/18865
Acceder
id CONICETDig_86dd04ff0140f5e812683cd9a35ec69f
oai_identifier_str oai:ri.conicet.gov.ar:11336/18865
network_acronym_str CONICETDig
repository_id_str 3498
network_name_str CONICET Digital (CONICET)
spelling Inference of Demographic Attributes based on Mobile Phone Usage Patterns and Social Network TopologySarraute, CarlosBrea, JorgeBurroni, JavierBlanc, PabloSocial Network AnalysisMobile Phone Social NetworkCall Detail RecordsGraph MiningDemographicsHomophilyhttps://purl.org/becyt/ford/1.2https://purl.org/becyt/ford/1Mobile phone usage provides a wealth of information, which can be used to better understand the demographic structure of a population. In this paper, we focus on the population of Mexican mobile phone users. We first present an observational study of mobile phone usage according to gender and age groups. We are able to detect significant differences in phone usage among different subgroups of the population. We then study the performance of different machine learning (ML) methods to predict demographic features (namely, age and gender) of unlabeled users by leveraging individual calling patterns, as well as the structure of the communication graph. We show how a specific implementation of a diffusion model, harnessing the graph structure, has significantly better performance over other node-based standard ML methods. We provide details of the methodology together with an analysis of the robustness of our results to changes in the model parameters. Furthermore, by carefully examining the topological relations of the training nodes (seed nodes) to the rest of the nodes in the network, we find topological metrics which have a direct influence on the performance of the algorithm.Fil: Sarraute, Carlos. Grandata Labs; ArgentinaFil: Brea, Jorge. Grandata Labs; ArgentinaFil: Burroni, Javier. Grandata Labs; ArgentinaFil: Blanc, Pablo. Consejo Nacional de Investigaciones Científicas y Técnicas. Oficina de Coordinación Administrativa Ciudad Universitaria. Instituto de Investigaciones Matemáticas "Luis A. Santalo". Universidad de Buenos Aires. Facultad de Ciencias Exactas y Naturales. Instituto de Investigaciones Matemáticas "Luis A. Santalo"; ArgentinaSpringer2015-12info:eu-repo/semantics/articleinfo:eu-repo/semantics/publishedVersionhttp://purl.org/coar/resource_type/c_6501info:ar-repo/semantics/articuloapplication/pdfapplication/pdfhttp://hdl.handle.net/11336/18865Sarraute, Carlos; Brea, Jorge; Burroni, Javier; Blanc, Pablo; Inference of Demographic Attributes based on Mobile Phone Usage Patterns and Social Network Topology ; Springer; Social Network Analysis and Mining; 5; 12-2015; 1-16; 391869-54501869-5469CONICET DigitalCONICETenginfo:eu-repo/semantics/altIdentifier/url/http://link.springer.com/article/10.1007/s13278-015-0277-xinfo:eu-repo/semantics/altIdentifier/doi/10.1007/s13278-015-0277-xinfo:eu-repo/semantics/openAccesshttps://creativecommons.org/licenses/by-nc-sa/2.5/ar/reponame:CONICET Digital (CONICET)instname:Consejo Nacional de Investigaciones Científicas y Técnicas2025-09-29T09:39:58Zoai:ri.conicet.gov.ar:11336/18865instacron: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 09:39:59.201CONICET Digital (CONICET) - Consejo Nacional de Investigaciones Científicas y Técnicasfalse
dc.title.none.fl_str_mv Inference of Demographic Attributes based on Mobile Phone Usage Patterns and Social Network Topology
title Inference of Demographic Attributes based on Mobile Phone Usage Patterns and Social Network Topology
spellingShingle Inference of Demographic Attributes based on Mobile Phone Usage Patterns and Social Network Topology
Sarraute, Carlos
Social Network Analysis
Mobile Phone Social Network
Call Detail Records
Graph Mining
Demographics
Homophily
title_short Inference of Demographic Attributes based on Mobile Phone Usage Patterns and Social Network Topology
title_full Inference of Demographic Attributes based on Mobile Phone Usage Patterns and Social Network Topology
title_fullStr Inference of Demographic Attributes based on Mobile Phone Usage Patterns and Social Network Topology
title_full_unstemmed Inference of Demographic Attributes based on Mobile Phone Usage Patterns and Social Network Topology
title_sort Inference of Demographic Attributes based on Mobile Phone Usage Patterns and Social Network Topology
dc.creator.none.fl_str_mv Sarraute, Carlos
Brea, Jorge
Burroni, Javier
Blanc, Pablo
author Sarraute, Carlos
author_facet Sarraute, Carlos
Brea, Jorge
Burroni, Javier
Blanc, Pablo
author_role author
author2 Brea, Jorge
Burroni, Javier
Blanc, Pablo
author2_role author
author
author
dc.subject.none.fl_str_mv Social Network Analysis
Mobile Phone Social Network
Call Detail Records
Graph Mining
Demographics
Homophily
topic Social Network Analysis
Mobile Phone Social Network
Call Detail Records
Graph Mining
Demographics
Homophily
purl_subject.fl_str_mv https://purl.org/becyt/ford/1.2
https://purl.org/becyt/ford/1
dc.description.none.fl_txt_mv Mobile phone usage provides a wealth of information, which can be used to better understand the demographic structure of a population. In this paper, we focus on the population of Mexican mobile phone users. We first present an observational study of mobile phone usage according to gender and age groups. We are able to detect significant differences in phone usage among different subgroups of the population. We then study the performance of different machine learning (ML) methods to predict demographic features (namely, age and gender) of unlabeled users by leveraging individual calling patterns, as well as the structure of the communication graph. We show how a specific implementation of a diffusion model, harnessing the graph structure, has significantly better performance over other node-based standard ML methods. We provide details of the methodology together with an analysis of the robustness of our results to changes in the model parameters. Furthermore, by carefully examining the topological relations of the training nodes (seed nodes) to the rest of the nodes in the network, we find topological metrics which have a direct influence on the performance of the algorithm.
Fil: Sarraute, Carlos. Grandata Labs; Argentina
Fil: Brea, Jorge. Grandata Labs; Argentina
Fil: Burroni, Javier. Grandata Labs; Argentina
Fil: Blanc, Pablo. Consejo Nacional de Investigaciones Científicas y Técnicas. Oficina de Coordinación Administrativa Ciudad Universitaria. Instituto de Investigaciones Matemáticas "Luis A. Santalo". Universidad de Buenos Aires. Facultad de Ciencias Exactas y Naturales. Instituto de Investigaciones Matemáticas "Luis A. Santalo"; Argentina
description Mobile phone usage provides a wealth of information, which can be used to better understand the demographic structure of a population. In this paper, we focus on the population of Mexican mobile phone users. We first present an observational study of mobile phone usage according to gender and age groups. We are able to detect significant differences in phone usage among different subgroups of the population. We then study the performance of different machine learning (ML) methods to predict demographic features (namely, age and gender) of unlabeled users by leveraging individual calling patterns, as well as the structure of the communication graph. We show how a specific implementation of a diffusion model, harnessing the graph structure, has significantly better performance over other node-based standard ML methods. We provide details of the methodology together with an analysis of the robustness of our results to changes in the model parameters. Furthermore, by carefully examining the topological relations of the training nodes (seed nodes) to the rest of the nodes in the network, we find topological metrics which have a direct influence on the performance of the algorithm.
publishDate 2015
dc.date.none.fl_str_mv 2015-12
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/11336/18865
Sarraute, Carlos; Brea, Jorge; Burroni, Javier; Blanc, Pablo; Inference of Demographic Attributes based on Mobile Phone Usage Patterns and Social Network Topology ; Springer; Social Network Analysis and Mining; 5; 12-2015; 1-16; 39
1869-5450
1869-5469
CONICET Digital
CONICET
url http://hdl.handle.net/11336/18865
identifier_str_mv Sarraute, Carlos; Brea, Jorge; Burroni, Javier; Blanc, Pablo; Inference of Demographic Attributes based on Mobile Phone Usage Patterns and Social Network Topology ; Springer; Social Network Analysis and Mining; 5; 12-2015; 1-16; 39
1869-5450
1869-5469
CONICET Digital
CONICET
dc.language.none.fl_str_mv eng
language eng
dc.relation.none.fl_str_mv info:eu-repo/semantics/altIdentifier/url/http://link.springer.com/article/10.1007/s13278-015-0277-x
info:eu-repo/semantics/altIdentifier/doi/10.1007/s13278-015-0277-x
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/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_ 1844613264473849856
score 13.070432

Publicaciones similares