  In many applications it is of interest to identify anomalous behavior within
a dynamic interacting system. Such anomalous interactions are reflected by
structural changes in the network representation of the system. We propose and
investigate the use of a dynamic version of the degree corrected stochastic
block model (DCSBM) to model and monitor dynamic networks that undergo a
significant structural change. We apply statistical process monitoring
techniques to the estimated parameters of the DCSBM to identify significant
structural changes in the network. Application of our surveillance strategy to
the dynamic U.S. Senate co-voting network reveals that we are able to detect
significant changes in the network that reflect both times of cohesion and
times of polarization among Republican and Democratic party members. These
findings provide valuable insight about the evolution of the bipartisan
political system in the United States. Our analysis demonstrates that the
dynamic DCSBM monitoring procedure effectively detects local and global
structural changes in dynamic networks. The DCSBM approach is an example of a
more general framework that combines parametric random graph models and
statistical process monitoring techniques for network surveillance.
