Max-Margin Nonparametric Latent Feature Models for Link Prediction

Understand

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.