Modeling and Topology Estimation of Low Rank Dynamical Networks

2025/11/10 by Wenqi Cao, Aming Li, Cao, Wenqi +1
Computer Science · Engineering · #Neural Networks and Applications #Neural Networks and Reservoir Computing #Sparse and Compressive Sensing Techniques

paper · pdf · doi:10.48550/arxiv.2511.06674

Abstract

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.

Citations

• On the identification of ARMA graphical models
• Deep networks for system identification: a Survey
• Identification of Low Rank Vector Processes
• Modeling of Low Rank Time Series
• A Reconstruction algorithm for an unknown network

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