Modeling and Topology Estimation of Low Rank Dynamical Networks

• URL: http://arxiv.org/abs/2511.06674v1
• Date: Mon, 10 Nov 2025 03:42:35 GMT
• Title: Modeling and Topology Estimation of Low Rank Dynamical Networks
• Authors: Wenqi Cao, Aming Li,
• Abstract summary: We propose a low rank dynamical network model which ensures identifiability.<n>Building on this theoretical result, we develop a consistent method for estimating all network edges.
• Score: 0.21485350418225238
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• 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.

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