An Ensemble Approach to Link Prediction.

A network with $n$<alternatives> <inline-graphic xlink:href="ma-ieq1-2730207.gif"/></alternatives> nodes contains $O(n^2)$<alternatives><inline-graphic xlink:href="ma-ieq2-2730207.gif"/> </alternatives> possible links. Even for networks of modest size, it is often difficult to evaluate all pairwise possibilities for links in a meaningful way. Further, even though link prediction is closely related to missing value estimation problems, it is often difficult to use sophisticated models such as latent factor methods because of their computational complexity on large networks. Hence, most known link prediction methods are designed for evaluating the link propensity on a specified subset of links, rather than on the entire networks. In practice, however, it is essential to perform an exhaustive search over the entire networks. In this article, we propose an ensemble enabled approach to scaling up link prediction, by decomposing traditional link prediction problems into subproblems of smaller size. These subproblems are each solved with latent factor models, which can be effectively implemented on networks of modest size. By incorporating with the characteristics of link prediction, the ensemble approach further reduces the sizes of subproblems without sacrificing its prediction accuracy. The ensemble enabled approach has several advantages in terms of performance, and our experimental results demonstrate the effectiveness and scalability of our approach. [ABSTRACT FROM AUTHOR]