  We consider linear time-invariant networks with unknown topology where only a
manifest subset of the nodes can be directly actuated and measured while the
state of the remaining latent nodes and their number are unknown. Our goal is
to identify the transfer function of the manifest subnetwork and determine
whether interactions between manifest nodes are direct or mediated by latent
nodes. We show that, if there are no inputs to the latent nodes, the manifest
transfer function can be approximated arbitrarily well in the H-infinity norm
sense by the transfer function of an auto-regressive model and present a
least-squares estimation method to construct the auto-regressive model from
measured data. We show that the least-squares auto-regressive method guarantees
an arbitrarily small H-infinity norm error in the approximation of the manifest
transfer function, exponentially decaying once the model order exceeds a
certain threshold. Finally, we show that when the latent subnetwork is acyclic,
the proposed method achieves perfect identification of the manifest transfer
function above a specific model order as the length of the data increases.
Various examples illustrate our results.
