Tomography of Large Adaptive Networks under the Dense Latent Regime : Invited Paper

This work examines the problem of graph learning over a diffusion network when measurements can only be gathered from a limited fraction of agents (latent regime). Under this setting, most works in the literature rely on a degree of sparsity to provide guarantees of consistent graph recovery. This work moves away from this condition and shows that, even under dense connectivity, the Granger estimator ensures an identifiability gap that enables the discrimination between connected and disconnected nodes within the observable subnetwork.