Link prediction using supervised learning

Social network analysis has attracted much attention in recent years. Link prediction is a key research direction within this area. In this paper, we study link prediction as a supervised learning task. Along the way, we identify a set of features that are key to the performance under the supervised learning setup. The identified features are very easy to compute, and at the same time surprisingly e#ective in solving the link prediction problem. We also explain the e#ectiveness of the features from their class density distribution. Then we compare di#erent classes of supervised learning algorithms in terms of their prediction performance using various performance metrics, such as accuracy, precision-recall,…