Predicting Unseen Links Using Learning-based Matrix Completion

Researchers have noticed the AS-level Internet topology that can be observed from the current measurement infrastructure is far from complete, which means researchers have to deploy more measurement vantage points (VPs) and conduct measurements for more source/destination pairs to fully understand the whole Internet. Unfortunately, it is known that blindly deploying more points and conducting more measurements to achieve the goal is inefficient, if not infeasible. In this paper, we try to improve the efficiency by predicting where unseen AS links might be located from the observed AS paths to guide the measurements towards a more complete AS-level topology. We formulate the prediction of unseen links as a matrix completion problem. However, the traditional matrix completion methods have limited learning capacities and cannot deal with the complex constraints on the underlying topology. We develop a learning-based matrix completion method specifically for the unseen AS link prediction problem. The method exploits a neural network and utilizes side-information which is carefully chosen from AS attributes based on our understanding on Internet peering practices, therefore our method is able to learn more expressive latent vectors and achieves outstanding prediction performance in our scenario. Experiments performed on a real-world dataset show the prediction results can achieve a high AUC (Area Under the Receiver Operating Characteristic Curve) of 0.834.