  We propose a probabilistic framework for modelling and exploring the latent
structure of relational data. Given feature information for the nodes in a
network, the scalable deep generative relational model (SDREM) builds a deep
network architecture that can approximate potential nonlinear mappings between
nodes' feature information and the nodes' latent representations. Our
contribution is two-fold: (1) We incorporate high-order neighbourhood structure
information to generate the latent representations at each node, which vary
smoothly over the network. (2) Due to the Dirichlet random variable structure
of the latent representations, we introduce a novel data augmentation trick
which permits efficient Gibbs sampling. The SDREM can be used for large sparse
networks as its computational cost scales with the number of positive links. We
demonstrate its competitive performance through improved link prediction
performance on a range of real-world datasets.
