  Topology learning is an important problem in dynamical systems with
implications to security and optimal control. The majority of prior work in
consistent topology estimation relies on dynamical systems excited by
temporally uncorrelated processes. In this article, we present a novel
algorithm for guaranteed topology learning, in networks that are excited by
temporally colored, cyclostationary processes. Furthermore, unlike prior work,
the framework applies to linear dynamic system with complex valued
dependencies. In the second part of the article, we analyze conditions for
consistent topology learning for bidirected radial networks when a subset of
the network is unobserved. Here, few agents are unobserved and the full
topology along with unobserved nodes are recovered from observed agents data
alone. Our theoretical contributions are validated on test networks.
