Chance-Constrained Programs for Link Prediction

In this paper, we consider the link prediction problem, where we are given a partial snapshot of a network at some time and the goal is to predict additional links at a later time. The accuracy of the current prediction methods is quite low due to the extreme class skew and the large number of potential links. In this paper, we describe learning algorithms based on chance constrained programs and show that they exhibit all the properties needed for a good link predictor, namely, allow preferential bias to positive or negative class; handle skewness in the data; and scale to large networks. Our experimental results on three real-world coauthorship networks show significant improvement in prediction accuracy over baseline algorithms.