title: Link prediction in multiplex online social networks.
abstract: 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%.
authors.affiliation: School of Engineering , RMIT University , Melbourne, Victoria , Australia.
authors.forename: Mahdi
authors.initials: M
authors.lastname: Jalili
authors.affiliation: Department of Computer Engineering , Sharif University of Technology , Tehran , Iran.
authors.forename: Yasin
authors.initials: Y
authors.lastname: Orouskhani
authors.affiliation: Department of Computer Science , University of California , Riverside, CA , USA.
authors.forename: Milad
authors.initials: M
authors.lastname: Asgari
authors.affiliation: Department of Computer Science , University of Southern California , Los Angeles, CA , USA.
authors.forename: Nazanin
authors.initials: N
authors.lastname: Alipourfard
authors.affiliation: Faculty of Natural Sciences and Mathematics, University of Maribor, Maribor, Slovenia; Center for Applied Mathematics and Theoretical Physics, University of Maribor, Maribor, Slovenia.
authors.forename: Matjaž
authors.identifier: 0000-0002-3087-541X
authors.initials: M
authors.lastname: Perc
keywords: complex networks; link prediction; machine learning; signed networks; social networks