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

A Poisson Gamma Probabilistic Model for Latent Node-group Memberships in Dynamic Networks