  We present a probabilistic model for learning from dynamic relational data,
wherein the observed interactions among networked nodes are modeled via the
Bernoulli Poisson link function, and the underlying network structure are
characterized by nonnegative latent node-group memberships, which are assumed
to be gamma distributed. The latent memberships evolve according to Markov
processes. The optimal number of latent groups can be determined by data
itself. The computational complexity of our method scales with the number of
non-zero links, which makes it scalable to large sparse dynamic relational
data. We present batch and online Gibbs sampling algorithms to perform model
inference. Finally, we demonstrate the model's performance on both synthetic
and real-world datasets compared to state-of-the-art methods.
