Please use this identifier to cite or link to this item: https://doi.org/10.1016/j.neucom.2011.06.036
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dc.titleRelationship strength estimation for online social networks with the study on Facebook
dc.contributor.authorZhao, X.
dc.contributor.authorYuan, J.
dc.contributor.authorLi, G.
dc.contributor.authorChen, X.
dc.contributor.authorLi, Z.
dc.date.accessioned2013-07-04T07:36:28Z
dc.date.available2013-07-04T07:36:28Z
dc.date.issued2012
dc.identifier.citationZhao, X., Yuan, J., Li, G., Chen, X., Li, Z. (2012). Relationship strength estimation for online social networks with the study on Facebook. Neurocomputing 95 : 89-97. ScholarBank@NUS Repository. https://doi.org/10.1016/j.neucom.2011.06.036
dc.identifier.issn09252312
dc.identifier.urihttp://scholarbank.nus.edu.sg/handle/10635/39210
dc.description.abstractOnline social network has become a popular way for users to express themselves, connect and share information with each other. However, in online social networks, the connections between different users are all in binary status, which neglects the relationship strengths between them. Meanwhile, the relationship strength between different users is activity field specific. In different activity fields, such as traveling, shopping, and sport, the relationship strengths between the same users may vary significantly. Therefore, in this paper we propose a general framework to measure the relationship strengths between different users, taking consideration not only the user's profile information but also the interaction activities and the activity fields. We conduct the experiments on Facebook dataset and the results show that the proposed framework is promising and can be used to improve the performances of various applications. © 2012 Elsevier B.V..
dc.description.urihttp://libproxy1.nus.edu.sg/login?url=http://dx.doi.org/10.1016/j.neucom.2011.06.036
dc.sourceScopus
dc.subjectActivity field
dc.subjectGraphical model
dc.subjectInteraction activity
dc.subjectLatent Dirichlet Allocation
dc.subjectOnline social network
dc.subjectRelationship strength
dc.subjectUser's profile information
dc.typeArticle
dc.contributor.departmentCOMPUTER SCIENCE
dc.description.doi10.1016/j.neucom.2011.06.036
dc.description.sourcetitleNeurocomputing
dc.description.volume95
dc.description.page89-97
dc.description.codenNRCGE
dc.identifier.isiut000307152800012
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