  Most real-world networks evolve over time. Existing literature proposes
models for dynamic networks that are either unlabeled or assumed to have a
single membership structure. On the other hand, a new family of Mixed
Membership Stochastic Block Models (MMSBM) allows to model static labeled
networks under the assumption of mixed-membership clustering. In this work, we
propose to extend this later class of models to infer dynamic labeled networks
under a mixed membership assumption. Our approach takes the form of a temporal
prior on the model's parameters. It relies on the single assumption that
dynamics are not abrupt. We show that our method significantly differs from
existing approaches, and allows to model more complex systems --dynamic labeled
networks. We demonstrate the robustness of our method with several experiments
on both synthetic and real-world datasets. A key interest of our approach is
that it needs very few training data to yield good results. The performance
gain under challenging conditions broadens the variety of possible applications
of automated learning tools --as in social sciences, which comprise many fields
where small datasets are a major obstacle to the introduction of machine
learning methods.
