20.500.12556/DKUM-67238 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 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%. social networks complex networks signed networks link prediction machine learning socialna omrežja družbena omrežja kompleksna omrežja podpisana omrežja napovedovanje povezav strojno učenje true true true Angleški jezik Slovenski jezik Članek v reviji 2017-08-08 12:44:17 2017-08-08 14:54:04 2024-03-18 13:38:46 2019-07-11 16:13:32 2017 0 0 str. 1-11 št. 2 Letn. 4 2017 0000-00-00 Zaloznikova Objavljeno NiDoloceno 0000-00-00 0000-00-00 0000-00-00 Y507-6544 53 3219791 22983432 10.1098/rsos.160863 Y507-6544 URN:SI:UM:DK:0WOMC3GN Royal_Society_Open_Science_2017_Jalili_et_al._Link_prediction_in_multiplex_online_social_networks.pdf Royal_Society_Open_Science_2017_Jalili_et_al._Link_prediction_in_multiplex_online_social_networks.pdf 1 67C67B181B9FBD9EBBAE9A33F3FC041F 1dff40b7476896c5f0681cc9ab983f55076c3e300a7330d627023a44bad4c9eb 7e49ed01-7c0e-11eb-bb7a-00155d0001ca 20.500.12556/dkum/d68e30e1-40bc-40f7-9b1d-4cc941616c6c https://dk.um.si/Dokument.php?lang=slv&id=115001 http://rsos.royalsocietypublishing.org/lookup/doi/10.1098/rsos.160863 1 https://dk.um.si/Dokument.php?lang=slv&id=115000 Fakulteta za naravoslovje in matematiko 0 0 0