Consistent Tomography over Diffusion Networks under the Low-Observability Regime

This work considers a diffusion network responding to streaming data, and studies the problem of identifying the topology of a subnetwork of observable agents by tracking their output measurements. Topology inference from indirect and/or incomplete datasets (network tomography) is in general an ill-posed problem. Under an appropriate Erdos-Renyi random graph model for the unobserved part, the problem of network tomography is well-posed in the thermodynamic limit: when the number of network agents grows to infinity, any arbitrary subnetwork topology associated with the observed agents can be recovered with high probability.