State-Space Network Topology Identification from Partial Observations

Journal article (2020)

Authors

Mario Coutino Signal Processing Systems - EEMCS
E. Isufi University of Pennsylvania
Takanori Maehara RIKEN Center for Emergent Matter Science (CEMS)
G.J.T. Leus Signal Processing Systems - EEMCS
Research Group
Signal Processing Systems (EEMCS) (TU Delft)
Copyright: © 2020 Mario Coutino, E. Isufi, Takanori Maehara, G.J.T. Leus
DOI: https://doi.org/10.1109/TSIPN.2020.2975393
Graph signal processing State-space models Inverse eigenvalue problems Network topology identification Signal processing over networks Inverse eigenvalue problems
More Info
expand_more

Published Date

2020

Language

English

Reuse Rights

Other than for strictly personal use, it is not permitted to download, forward or distribute the text or part of it, without the consent of the author(s) and/or copyright holder(s), unless the work is under an open content license such as Creative Commons.

Faculty

Electrical Engineering, Mathematics and Computer Science

Department

Microelectronics

Research Group

Signal Processing Systems

Abstract

In this article, we explore the state-space formulation of a network process to recover from partial observations the network topology that drives its dynamics. To do so, we employ subspace techniques borrowed from system identification literature and extend them to the network topology identification problem. This approach provides a unified view of network control and signal processing on graphs. In addition, we provide theoretical guarantees for the recovery of the topological structure of a deterministic continuous-time linear dynamical system from input-output observations even when the input and state interaction networks are different. Our mathematical analysis is accompanied by an algorithm for identifying from data,a network topology consistent with the system dynamics and conforms to the prior information about the underlying structure. The proposed algorithm relies on alternating projections and is provably convergent. Numerical results corroborate the theoretical findings and the applicability of the proposed algorithm.