  The Dirichlet Belief Network~(DirBN) has been recently proposed as a
promising approach in learning interpretable deep latent representations for
objects. In this work, we leverage its interpretable modelling architecture and
propose a deep dynamic probabilistic framework -- the Recurrent Dirichlet
Belief Network~(Recurrent-DBN) -- to study interpretable hidden structures from
dynamic relational data. The proposed Recurrent-DBN has the following merits:
(1) it infers interpretable and organised hierarchical latent structures for
objects within and across time steps; (2) it enables recurrent long-term
temporal dependence modelling, which outperforms the one-order Markov
descriptions in most of the dynamic probabilistic frameworks. In addition, we
develop a new inference strategy, which first upward-and-backward propagates
latent counts and then downward-and-forward samples variables, to enable
efficient Gibbs sampling for the Recurrent-DBN. We apply the Recurrent-DBN to
dynamic relational data problems. The extensive experiment results on
real-world data validate the advantages of the Recurrent-DBN over the
state-of-the-art models in interpretable latent structure discovery and
improved link prediction performance.
