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Link prediction in multiplex online social networks

Online social networks play a major role in modern societies, and they have shaped the way social relationships evolve. Link prediction in social networks has many potential applications such as recommending new items to users, friendship suggestion and discovering spurious connections. Many real so...

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Detalles Bibliográficos
Autores principales: Jalili, Mahdi, Orouskhani, Yasin, Asgari, Milad, Alipourfard, Nazanin, Perc, Matjaž
Formato: Online Artículo Texto
Lenguaje:English
Publicado: The Royal Society Publishing 2017
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5367313/
https://www.ncbi.nlm.nih.gov/pubmed/28386441
http://dx.doi.org/10.1098/rsos.160863
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author Jalili, Mahdi
Orouskhani, Yasin
Asgari, Milad
Alipourfard, Nazanin
Perc, Matjaž
author_facet Jalili, Mahdi
Orouskhani, Yasin
Asgari, Milad
Alipourfard, Nazanin
Perc, Matjaž
author_sort Jalili, Mahdi
collection PubMed
description Online social networks play a major role in modern societies, and they have shaped the way social relationships evolve. Link prediction in social networks has many potential applications such as recommending new items to users, friendship suggestion and discovering spurious connections. Many real social networks evolve the connections in multiple layers (e.g. multiple social networking platforms). In this article, we study the link prediction problem in multiplex networks. As an example, we consider a multiplex network of Twitter (as a microblogging service) and Foursquare (as a location-based social network). We consider social networks of the same users in these two platforms and develop a meta-path-based algorithm for predicting the links. The connectivity information of the two layers is used to predict the links in Foursquare network. Three classical classifiers (naive Bayes, support vector machines (SVM) and K-nearest neighbour) are used for the classification task. Although the networks are not highly correlated in the layers, our experiments show that including the cross-layer information significantly improves the prediction performance. The SVM classifier results in the best performance with an average accuracy of 89%.
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spelling pubmed-53673132017-04-06 Link prediction in multiplex online social networks Jalili, Mahdi Orouskhani, Yasin Asgari, Milad Alipourfard, Nazanin Perc, Matjaž R Soc Open Sci Engineering Online social networks play a major role in modern societies, and they have shaped the way social relationships evolve. Link prediction in social networks has many potential applications such as recommending new items to users, friendship suggestion and discovering spurious connections. Many real social networks evolve the connections in multiple layers (e.g. multiple social networking platforms). In this article, we study the link prediction problem in multiplex networks. As an example, we consider a multiplex network of Twitter (as a microblogging service) and Foursquare (as a location-based social network). We consider social networks of the same users in these two platforms and develop a meta-path-based algorithm for predicting the links. The connectivity information of the two layers is used to predict the links in Foursquare network. Three classical classifiers (naive Bayes, support vector machines (SVM) and K-nearest neighbour) are used for the classification task. Although the networks are not highly correlated in the layers, our experiments show that including the cross-layer information significantly improves the prediction performance. The SVM classifier results in the best performance with an average accuracy of 89%. The Royal Society Publishing 2017-02-08 /pmc/articles/PMC5367313/ /pubmed/28386441 http://dx.doi.org/10.1098/rsos.160863 Text en © 2017 The Authors. http://creativecommons.org/licenses/by/4.0/ Published by the Royal Society under the terms of the Creative Commons Attribution License http://creativecommons.org/licenses/by/4.0/, which permits unrestricted use, provided the original author and source are credited.
spellingShingle Engineering
Jalili, Mahdi
Orouskhani, Yasin
Asgari, Milad
Alipourfard, Nazanin
Perc, Matjaž
Link prediction in multiplex online social networks
title Link prediction in multiplex online social networks
title_full Link prediction in multiplex online social networks
title_fullStr Link prediction in multiplex online social networks
title_full_unstemmed Link prediction in multiplex online social networks
title_short Link prediction in multiplex online social networks
title_sort link prediction in multiplex online social networks
topic Engineering
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5367313/
https://www.ncbi.nlm.nih.gov/pubmed/28386441
http://dx.doi.org/10.1098/rsos.160863
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