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 effective in solving the link prediction problem. We also explain the effectiveness of the features from their class density distribution. Then we compare different classes of supervised learning algorithms in terms of their prediction performance using various performance metrics, such as accuracy, precision-recall, F-values, squared error etc. with a 5-fold cross validation. Our results on two practical social network datasets shows that most of the well-known classification algorithms (decision tree, k-NN, multilayer perceptron, SVM, RBF network) can predict links with comparable performances, but SVM outperforms all of them with narrow margin in all performance measures. Again, ranking of features with popular feature ranking algorithms shows that a small subset of features always plays a significant role in link prediction.