Network Identification with Latent Nodes via Auto-Regressive Models
For control and network science researchers, this provides a theoretically grounded method to identify direct vs. mediated interactions in networks with unknown latent nodes, though the results are limited to linear time-invariant systems and specific latent structures.
The paper addresses network identification in linear time-invariant systems with latent nodes, showing that the manifest transfer function can be approximated arbitrarily well by an auto-regressive model using least-squares estimation, with exponentially decaying H-infinity error beyond a threshold model order. Perfect identification is achieved for acyclic latent subnetworks as data length increases.
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.