  Sociotechnological and geospatial processes exhibit time varying structure
that make insight discovery challenging. To detect abnormal moments in these
processes, a definition of `normal' must be established. 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 `normal' seasonal
processes from data when they are unknown. An outline of anomaly detection
experiments to be completed over Enron emails and New York City taxi trips is
presented.
