Belief Dynamics in Complex Social Networks

Autores
Gallo, Fabio R.; Abad Santos, Natalia; Simari, Gerardo I.; Martínez, María Vanina; Falappa, Marcelo Alejandro
Año de publicación
2016
Idioma
inglés
Tipo de recurso
documento de conferencia
Estado
versión publicada
Descripción
People are becoming increasingly more connected to each other in social media networks. These networks are complex because in general there can be many di fferent types of relations, as well as di fferent degrees of strength for each one; moreover, these relations are dynamic because they can change over time. In this context, users' knowledge flows over the network, and modeling how this occurs - or can possibly occur - is therefore of great interest from a knowledge representation and reasoning perspective. In this paper, we focus on the problem of how a single user's knowledge base changes when exposed to a stream of news items coming from other members in the network. As a first step towards solving this problem, we identify possible solutions leveraging preexisting belief merging operators, and conclude that there is a gap that needs to be bridged between the application of such operators and a principled solution to the proposed problem.
Sociedad Argentina de Informática e Investigación Operativa (SADIO)
Materia
Ciencias Informáticas
social web
complex networks
belief merging operators
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:10915/56887

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network_name_str SEDICI (UNLP)
spelling Belief Dynamics in Complex Social NetworksGallo, Fabio R.Abad Santos, NataliaSimari, Gerardo I.Martínez, María VaninaFalappa, Marcelo AlejandroCiencias Informáticassocial webcomplex networksbelief merging operatorsPeople are becoming increasingly more connected to each other in social media networks. These networks are complex because in general there can be many di fferent types of relations, as well as di fferent degrees of strength for each one; moreover, these relations are dynamic because they can change over time. In this context, users' knowledge flows over the network, and modeling how this occurs - or can possibly occur - is therefore of great interest from a knowledge representation and reasoning perspective. In this paper, we focus on the problem of how a single user's knowledge base changes when exposed to a stream of news items coming from other members in the network. As a first step towards solving this problem, we identify possible solutions leveraging preexisting belief merging operators, and conclude that there is a gap that needs to be bridged between the application of such operators and a principled solution to the proposed problem.Sociedad Argentina de Informática e Investigación Operativa (SADIO)2016-09info:eu-repo/semantics/conferenceObjectinfo:eu-repo/semantics/publishedVersionObjeto de conferenciahttp://purl.org/coar/resource_type/c_5794info:ar-repo/semantics/documentoDeConferenciaapplication/pdf70-77http://sedici.unlp.edu.ar/handle/10915/56887enginfo:eu-repo/semantics/altIdentifier/url/http://45jaiio.sadio.org.ar/sites/default/files/ASAI-04_0.pdfinfo:eu-repo/semantics/altIdentifier/issn/2451-7585info: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:UNLP2025-09-29T11:06:12Zoai:sedici.unlp.edu.ar:10915/56887Institucionalhttp://sedici.unlp.edu.ar/Universidad públicaNo correspondehttp://sedici.unlp.edu.ar/oai/snrdalira@sedici.unlp.edu.arArgentinaNo correspondeNo correspondeNo correspondeopendoar:13292025-09-29 11:06:12.372SEDICI (UNLP) - Universidad Nacional de La Platafalse
dc.title.none.fl_str_mv Belief Dynamics in Complex Social Networks
title Belief Dynamics in Complex Social Networks
spellingShingle Belief Dynamics in Complex Social Networks
Gallo, Fabio R.
Ciencias Informáticas
social web
complex networks
belief merging operators
title_short Belief Dynamics in Complex Social Networks
title_full Belief Dynamics in Complex Social Networks
title_fullStr Belief Dynamics in Complex Social Networks
title_full_unstemmed Belief Dynamics in Complex Social Networks
title_sort Belief Dynamics in Complex Social Networks
dc.creator.none.fl_str_mv Gallo, Fabio R.
Abad Santos, Natalia
Simari, Gerardo I.
Martínez, María Vanina
Falappa, Marcelo Alejandro
author Gallo, Fabio R.
author_facet Gallo, Fabio R.
Abad Santos, Natalia
Simari, Gerardo I.
Martínez, María Vanina
Falappa, Marcelo Alejandro
author_role author
author2 Abad Santos, Natalia
Simari, Gerardo I.
Martínez, María Vanina
Falappa, Marcelo Alejandro
author2_role author
author
author
author
dc.subject.none.fl_str_mv Ciencias Informáticas
social web
complex networks
belief merging operators
topic Ciencias Informáticas
social web
complex networks
belief merging operators
dc.description.none.fl_txt_mv People are becoming increasingly more connected to each other in social media networks. These networks are complex because in general there can be many di fferent types of relations, as well as di fferent degrees of strength for each one; moreover, these relations are dynamic because they can change over time. In this context, users' knowledge flows over the network, and modeling how this occurs - or can possibly occur - is therefore of great interest from a knowledge representation and reasoning perspective. In this paper, we focus on the problem of how a single user's knowledge base changes when exposed to a stream of news items coming from other members in the network. As a first step towards solving this problem, we identify possible solutions leveraging preexisting belief merging operators, and conclude that there is a gap that needs to be bridged between the application of such operators and a principled solution to the proposed problem.
Sociedad Argentina de Informática e Investigación Operativa (SADIO)
description People are becoming increasingly more connected to each other in social media networks. These networks are complex because in general there can be many di fferent types of relations, as well as di fferent degrees of strength for each one; moreover, these relations are dynamic because they can change over time. In this context, users' knowledge flows over the network, and modeling how this occurs - or can possibly occur - is therefore of great interest from a knowledge representation and reasoning perspective. In this paper, we focus on the problem of how a single user's knowledge base changes when exposed to a stream of news items coming from other members in the network. As a first step towards solving this problem, we identify possible solutions leveraging preexisting belief merging operators, and conclude that there is a gap that needs to be bridged between the application of such operators and a principled solution to the proposed problem.
publishDate 2016
dc.date.none.fl_str_mv 2016-09
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