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Topology learning of networked dynamical systems is an important problem withimplications to optimal control, decision-making over networks, cybersecurityand safety. The majority of prior work in consistent topology estimation relieson dynamical systems excited by temporally uncorrelated processes. In thisarticle, we present a novel algorithm for guaranteed topology learning ofnetworks that are excited by temporally (colored) cyclostationary processes,which encompasses a wide range of temporal correlation including wide-sensestationarity. Furthermore, unlike prior work, the framework applies to lineardynamic system with complex valued dependencies, and leverages group lassoregularization for effective learning of the network structure. In the secondpart of the article, we analyze conditions for consistent topology learning forbidirected tree networks when a subset of the network is unobserved. Here, thefull topology along with unobserved nodes are recovered from observed node'stime-series alone. Our theoretical contributions are validated on simulateddata as well as on real-world climate data.

Topology Learning of unknown Networked Linear Dynamical System excited by Cyclostationary inputs