![]() ![]() Although the networks are not highly correlated in the layers, our experiments show that including the cross-layer information significantly improves the prediction performance. Three classical classifiers (naive Bayes, support vector machines (SVM) and K-nearest neighbour) are used for the classification task. The connectivity information of the two layers is used to predict the links in Foursquare network. We consider social networks of the same users in these two platforms and develop a meta-path-based algorithm for predicting the links. As an example, we consider a multiplex network of Twitter (as a microblogging service) and Foursquare (as a location-based social network). In this article, we study the link prediction problem in multiplex networks. Many real social networks evolve the connections in multiple layers (e.g. ![]() Link prediction in social networks has many potential applications such as recommending new items to users, friendship suggestion and discovering spurious connections. Online social networks play a major role in modern societies, and they have shaped the way social relationships evolve. ![]()
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