Tomography of Large Adaptive Networks under the Dense Latent Regime
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 selling, 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.
WOS:000467845100378
2018-01-01
978-1-5386-9218-9
New York
Conference Record of the Asilomar Conference on Signals Systems and Computers
2144
2148
REVIEWED
Event name | Event place | Event date |
Pacific Grove, CA | Oct 28-Nov 01, 2018 | |