Multilayer Link Prediction in Online Social Networks

At present, people are communicating using various online social networks, each exhibiting different topology and structure. Link prediction is an important and difficult task in graph mining with a goal to predict the evolution of a social network using its topological features. However, nowadays it becomes even more important to predict the evolution of an interwoven (mutiplex) network structure by using network features from its constituent parts, i.e. in our case, different online social networks. In this work, we are using certain features from Twitter and Foursquare social networks to predict the link formation in both networks. The results show that the prediction rate depends not only on different node pair features and machine learning algorithms, but also on the properties of the target network on which we predict the link formation. We show that when predicting links in the Foursquare network the best obtained accuracy is above 90%, whereas when predicting the evolution of the Twitter network it is around 87%. We argue that the higher accuracy for link prediction in the Foursquare network is due to its locality nature. Finally, both results show an improvement in the prediction accuracy compared to the existing approaches for this dataset presented in the literature.