  We show that the steady-state variance as a performance measure for a class
of networked linear control systems is expressible as the summation of a
rational function over the Laplacian eigenvalues of the network graph.
Moreover, we characterize the role of connectivity thresholds for the feedback
(and observer) gain design of these networks. We use our framework to derive
bounds and scaling laws for the performance of the dynamical network. Our
approach generalizes and unifies the previous results on the performance
measure of these networks for the case of arbitrary nodal dynamics. We bring
extensions of our methodology for the case of decentralized observer-based
output feedback as well as a class of composite networks. Numerous examples
support our theoretical contributions.
