  Embedding dyadic data into a latent space has long been a popular approach to
modeling networks of all kinds. While clustering has been done using this
approach for static networks, this paper gives two methods of community
detection within dynamic network data, building upon the distance and
projection models previously proposed in the literature. Our proposed
approaches capture the time-varying aspect of the data, can model directed or
undirected edges, inherently incorporate transitivity and account for each
actor's individual propensity to form edges. We provide Bayesian estimation
algorithms, and apply these methods to a ranked dynamic friendship network and
world export/import data.
