  Link prediction is a fundamental task in statistical network analysis. Recent
advances have been made on learning flexible nonparametric Bayesian latent
feature models for link prediction. In this paper, we present a max-margin
learning method for such nonparametric latent feature relational models. Our
approach attempts to unite the ideas of max-margin learning and Bayesian
nonparametrics to discover discriminative latent features for link prediction.
It inherits the advances of nonparametric Bayesian methods to infer the unknown
latent social dimension, while for discriminative link prediction, it adopts
the max-margin learning principle by minimizing a hinge-loss using the linear
expectation operator, without dealing with a highly nonlinear link likelihood
function. For posterior inference, we develop an efficient stochastic
variational inference algorithm under a truncated mean-field assumption. Our
methods can scale up to large-scale real networks with millions of entities and
tens of millions of positive links. We also provide a full Bayesian
formulation, which can avoid tuning regularization hyper-parameters.
Experimental results on a diverse range of real datasets demonstrate the
benefits inherited from max-margin learning and Bayesian nonparametric
inference.
