# Modeling and Topology Estimation of Low Rank Dynamical Networks

arXiv: https://arxiv.org/abs/2511.06674 · HTML: https://mlsanity.com/paper/2511.06674/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 4 of its 10 references that are in the map.
- First posted on arXiv 10 Nov 2025.

## Builds on (most cited first, 4 of 4)

- [Deep networks for system identification: a Survey](https://mlsanity.com/paper/2301.12832/) (2023)
- [Identification of Low Rank Vector Processes](https://mlsanity.com/paper/2111.10899/) (2021)
- [On the identification of ARMA graphical models](https://mlsanity.com/paper/2308.14384/) (2023)
- [Modeling of Low Rank Time Series](https://mlsanity.com/paper/2109.11814/) (2021)

## Abstract (arXiv)

Conventional topology learning methods for dynamical networks become inapplicable to processes exhibiting low-rank characteristics. To address this, we propose the low rank dynamical network model which ensures identifiability. By employing causal Wiener filtering, we establish a necessary and sufficient condition that links the sparsity pattern of the filter to conditional Granger causality. Building on this theoretical result, we develop a consistent method for estimating all network edges. Simulation results demonstrate the parsimony of the proposed framework and consistency of the topology estimation approach.