Online social networks are the inseparable element of current modern societies and significantly influence forming and
consolidating social relationships. In nature, these networks are multiplex so that multiple links may exist between the same
two users across different social networks. In this paper, we study the ego-social features of multiplex links, spanning more
than one social networks and apply their structural and interaction features to the problem of link prediction. The link prediction
is applied for various cases in social networks such as new recommendations for users, friendship suggestions and
fake relations discovery. Most of the real-world social networks promote communications in multi-layers (for example, the
platform of multiple social networks). In this work, the problem of link prediction in multiple networks including Twitter (as
a microblogging service) and Foursquare (as a place-based social network) has been studied. We consider the users jointly
use both social network platforms and develop a classification algorithm for predicting the links. Hereto, the layers structural
information is considered to predict the links in Foursquare network. Technically, solving this classification problem
is accomplished through defining three sets of features based on nodal structure, ego-paths and meta-paths (SEM-Path).
Three classic classifiers such as ID3, SVM and LR are used for the classification problem in the SEM-Path method. Our
evaluations show that we can successfully predict links across social networking platforms. In fact, evaluations aim to shed
light on the implications of multiplexity for the link generation process. The SVM classifier outperforms other classifiers
with an average precision equal to 77.62%. Also, it has almost 1.5% superiority than the meta-path-based algorithm method