Using Rough Sets in Homophily Based Link Prediction in Online Social Networks

Online social networks are highly dynamic and sparse. One of the main problems in analyzing these networks is the problem of predicting the existence of links between users on these networks: Link prediction problem. Many researches have been conducted to predict links using variety of techniques like the decision tree and the logistic regression approaches. In this work, we will illustrate the use of rough set theory (RST) in predicting links over the Facebook social network based on homophilic features. Other classifiers are also employed in our work and compared to the rough set classifier.