{"ID":2843713,"CreatedAt":"2026-06-01T04:54:23.091178241Z","UpdatedAt":"2026-06-01T04:54:23.091178241Z","DeletedAt":null,"paper_url":"https://arxiv.org/abs/2511.06674","arxiv_id":"2511.06674","title":"Modeling and Topology Estimation of Low Rank Dynamical Networks","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.","short_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...","url_abs":"https://arxiv.org/abs/2511.06674","url_pdf":"https://arxiv.org/pdf/2511.06674v1","authors":"[\"Wenqi Cao\",\"Aming Li\"]","published":"2025-11-10T03:42:35Z","proceeding":"cs.GR","tasks":"[\"cs.GR\",\"stat.ML\"]","methods":"[]","has_code":false}