  Sociotechnological and geospatial processes exhibit time varying structure
that make insight discovery challenging. This paper proposes a new statistical
model for such systems, modeled as dynamic networks, to address this challenge.
It assumes that vertices fall into one of k types and that the probability of
edge formation at a particular time depends on the types of the incident nodes
and the current time. The time dependencies are driven by unique seasonal
processes, which many systems exhibit (e.g., predictable spikes in geospatial
or web traffic each day). The paper defines the model as a generative process
and an inference procedure to recover the seasonal processes from data when
they are unknown. Evaluation with synthetic dynamic networks show the recovery
of the latent seasonal processes that drive its formation.
