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

Diversion Detection in Partially Observed Nuclear Fuel Cycle Networks