Latent feature models for large-scale link prediction

Abstract

Link prediction is one of the most fundamental tasks in statistical network analysis, for which latent feature models have been widely used. As large-scale networks are available in various application domains, how to develop effective models and scalable algorithms becomes a new challenge. In this paper, we provide a review of the recent progress on latent feature models for the task of link prediction in large-scale networks, including the nonparametric Bayesian models which can automatically infer the latent social dimensions and the max-margin models which can learn strongly discriminative latent features for highly accurate predictions as well as dealing with the imbalance issue in large real networks. We also review the progress on scalable algorithms for posterior inference in such models, including stochastic variational methods and MCMC methods with data augmentation.