  A nuclear fuel cycle contains several facilities with different purposes such
as mining, conversion, enrichment, and fuel rod fabrication. These facilities
form a network, which is naturally sparse in the number of connections (i.e.,
edges) since not every facility directly interacts with all the others. Given
the knowledge of a network baseline, we are interested in detecting anomalous
activities in this network, which may signal the diversion of nuclear
materials. Anomalies can take the form of a new or missing edge or abnormal
rates of interaction. However, often it is not possible to observe the entire
network traffic directly due to some constraints such as cost, physical
limitations, or laws. By treating the unobserved network traffic as latent
variables, we propose estimators for the true network traffic, including the
anomalous activity, to use in testing for significant deviations from the
baseline. We provide simulation results of a simple network of facilities and
show that our estimators have superior performance over existing alternatives.
Additionally, we establish that while a good estimate of the network traffic is
necessary, perfect reconstruction is not required to effectively detect
anomalous network activity. Instead it suffices to detect perturbations within
the network at an aggregate or global scale.
