Supervised Prediction of Social Network Links Using Implicit Sources of Information

Ervin Tasnádi, Gábor Berend

Abstract: In this paper, we introduce a supervised machine learning framework for the link prediction problem. The social network we conducted our empirical evaluation on originates from the restaurant review portal, yelp.com. The proposed framework not only uses the structure of the social network to predict non-existing edges in it, but also makes use of further graphs that were constructed based on implicit information provided in the dataset. The implicit information we relied on includes the language use of the members of the social network and their ratings with respect the businesses they reviewed. Here, we also investigate the possibility of building supervised learning models to predict social links without relying on features derived from the structure of the social network itself, but based on such implicit information alone. Our empirical results not only revealed that the features derived from different sources of implicit information can be useful on their own, but also that incorporating them in a unified framework has the potential to improve classification results, as the different sources of implicit information can provide independent and useful views about the connectedness of users.