Social Events in a Time-Varying Mobile Phone Graph

Autores
Sarraute, Carlos; Brea, Jorge; Burroni, Javier; Wehmuth, Klaus; Ziviani, Arthur; Alvarez Hamelin, José I.
Año de publicación
2015
Idioma
inglés
Tipo de recurso
documento de conferencia
Estado
versión publicada
Descripción
The large-scale study of human mobility has been significantly enhanced over the last decade by the massive use of mobile phones in urban populations. Studying the activity of mobile phones allows us, not only to infer social networks between individuals, but also to observe the movements of these individuals in space and time. In this work, we investigate how these two related sources of information can be integrated within the context of detecting and analyzing large social events. We show that large social events can be characterized not only by an anomalous increase in activity of the antennas in the neighborhood of the event, but also by an increase in social relationships of the attendants present in the event. Moreover, having detected a large social event via increased antenna activity, we can use the network connections to infer whether an unobserved user was present at the event. More precisely, we address the following three challenges: (i) automatically detecting large social events via increased antenna activity; (ii) characterizing the social cohesion of the detected event; and (iii) analyzing the feasibility of inferring whether unobserved users were in the event.
Sociedad Argentina de Informática e Investigación Operativa (SADIO)
Materia
Ciencias Informáticas
big data
event antenna
Teléfono Celular
Nivel de accesibilidad
acceso abierto
Condiciones de uso
http://creativecommons.org/licenses/by-sa/3.0/
Repositorio
SEDICI (UNLP)
Institución
Universidad Nacional de La Plata
OAI Identificador
oai:sedici.unlp.edu.ar:10915/51979

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spelling Social Events in a Time-Varying Mobile Phone GraphSarraute, CarlosBrea, JorgeBurroni, JavierWehmuth, KlausZiviani, ArthurAlvarez Hamelin, José I.Ciencias Informáticasbig dataevent antennaTeléfono CelularThe large-scale study of human mobility has been significantly enhanced over the last decade by the massive use of mobile phones in urban populations. Studying the activity of mobile phones allows us, not only to infer social networks between individuals, but also to observe the movements of these individuals in space and time. In this work, we investigate how these two related sources of information can be integrated within the context of detecting and analyzing large social events. We show that large social events can be characterized not only by an anomalous increase in activity of the antennas in the neighborhood of the event, but also by an increase in social relationships of the attendants present in the event. Moreover, having detected a large social event via increased antenna activity, we can use the network connections to infer whether an unobserved user was present at the event. More precisely, we address the following three challenges: (i) automatically detecting large social events via increased antenna activity; (ii) characterizing the social cohesion of the detected event; and (iii) analyzing the feasibility of inferring whether unobserved users were in the event.Sociedad Argentina de Informática e Investigación Operativa (SADIO)2015-09info:eu-repo/semantics/conferenceObjectinfo:eu-repo/semantics/publishedVersionObjeto de conferenciahttp://purl.org/coar/resource_type/c_5794info:ar-repo/semantics/documentoDeConferenciaapplication/pdf43-49http://sedici.unlp.edu.ar/handle/10915/51979enginfo:eu-repo/semantics/altIdentifier/url/http://44jaiio.sadio.org.ar/sites/default/files/agranda43-49.pdfinfo:eu-repo/semantics/altIdentifier/issn/2451-7569info:eu-repo/semantics/openAccesshttp://creativecommons.org/licenses/by-sa/3.0/Creative Commons Attribution-ShareAlike 3.0 Unported (CC BY-SA 3.0)reponame:SEDICI (UNLP)instname:Universidad Nacional de La Platainstacron:UNLP2025-09-10T12:07:47Zoai:sedici.unlp.edu.ar:10915/51979Institucionalhttp://sedici.unlp.edu.ar/Universidad públicaNo correspondehttp://sedici.unlp.edu.ar/oai/snrdalira@sedici.unlp.edu.arArgentinaNo correspondeNo correspondeNo correspondeopendoar:13292025-09-10 12:07:48.114SEDICI (UNLP) - Universidad Nacional de La Platafalse
dc.title.none.fl_str_mv Social Events in a Time-Varying Mobile Phone Graph
title Social Events in a Time-Varying Mobile Phone Graph
spellingShingle Social Events in a Time-Varying Mobile Phone Graph
Sarraute, Carlos
Ciencias Informáticas
big data
event antenna
Teléfono Celular
title_short Social Events in a Time-Varying Mobile Phone Graph
title_full Social Events in a Time-Varying Mobile Phone Graph
title_fullStr Social Events in a Time-Varying Mobile Phone Graph
title_full_unstemmed Social Events in a Time-Varying Mobile Phone Graph
title_sort Social Events in a Time-Varying Mobile Phone Graph
dc.creator.none.fl_str_mv Sarraute, Carlos
Brea, Jorge
Burroni, Javier
Wehmuth, Klaus
Ziviani, Arthur
Alvarez Hamelin, José I.
author Sarraute, Carlos
author_facet Sarraute, Carlos
Brea, Jorge
Burroni, Javier
Wehmuth, Klaus
Ziviani, Arthur
Alvarez Hamelin, José I.
author_role author
author2 Brea, Jorge
Burroni, Javier
Wehmuth, Klaus
Ziviani, Arthur
Alvarez Hamelin, José I.
author2_role author
author
author
author
author
dc.subject.none.fl_str_mv Ciencias Informáticas
big data
event antenna
Teléfono Celular
topic Ciencias Informáticas
big data
event antenna
Teléfono Celular
dc.description.none.fl_txt_mv The large-scale study of human mobility has been significantly enhanced over the last decade by the massive use of mobile phones in urban populations. Studying the activity of mobile phones allows us, not only to infer social networks between individuals, but also to observe the movements of these individuals in space and time. In this work, we investigate how these two related sources of information can be integrated within the context of detecting and analyzing large social events. We show that large social events can be characterized not only by an anomalous increase in activity of the antennas in the neighborhood of the event, but also by an increase in social relationships of the attendants present in the event. Moreover, having detected a large social event via increased antenna activity, we can use the network connections to infer whether an unobserved user was present at the event. More precisely, we address the following three challenges: (i) automatically detecting large social events via increased antenna activity; (ii) characterizing the social cohesion of the detected event; and (iii) analyzing the feasibility of inferring whether unobserved users were in the event.
Sociedad Argentina de Informática e Investigación Operativa (SADIO)
description The large-scale study of human mobility has been significantly enhanced over the last decade by the massive use of mobile phones in urban populations. Studying the activity of mobile phones allows us, not only to infer social networks between individuals, but also to observe the movements of these individuals in space and time. In this work, we investigate how these two related sources of information can be integrated within the context of detecting and analyzing large social events. We show that large social events can be characterized not only by an anomalous increase in activity of the antennas in the neighborhood of the event, but also by an increase in social relationships of the attendants present in the event. Moreover, having detected a large social event via increased antenna activity, we can use the network connections to infer whether an unobserved user was present at the event. More precisely, we address the following three challenges: (i) automatically detecting large social events via increased antenna activity; (ii) characterizing the social cohesion of the detected event; and (iii) analyzing the feasibility of inferring whether unobserved users were in the event.
publishDate 2015
dc.date.none.fl_str_mv 2015-09
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dc.language.none.fl_str_mv eng
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eu_rights_str_mv openAccess
rights_invalid_str_mv http://creativecommons.org/licenses/by-sa/3.0/
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