Beyond Accuracy in Link Prediction

Abstract

Link prediction has mainly been addressed as an accuracy-targeting problem in social network analysis. We discuss different perspectives on the problem considering other dimensions and effects that the link prediction methods may have on the network where they are applied. Specifically, we consider the structural effects the methods can have if the predicted links are added to the network. We consider further utility dimensions beyond prediction accuracy, namely novelty and diversity. We adapt specific metrics from social network analysis, recommender systems and information retrieval, and we empirically observe the effect of a set of link prediction algorithms over Twitter data.