State-space based network topology identification

M. Coutino, E. Isufi, T. Maehara, G. Leus

Research output: Chapter in Book/Conference proceedings/Edited volumeConference contributionScientificpeer-review

4 Citations (Scopus)
36 Downloads (Pure)


In this work, we explore the state-space formulation of network processes to recover the underlying network structure (local connections). To do so, we employ subspace techniques borrowed from system identification literature and extend them to the network topology inference problem. This approach provides a unified view of the traditional network control theory and signal processing on networks. In addition, it provides theoretical guarantees for the recovery of the topological structure of a deterministic linear dynamical system from input-output observations even though the input and state evolution networks can differ.
Original languageEnglish
Title of host publication28th European Signal Processing Conference, EUSIPCO 2020 - Proceedings
Place of PublicationAmsterdam (Netherlands)
Number of pages5
ISBN (Electronic)978-9-0827-9705-3
Publication statusPublished - 1 Aug 2020
EventEUSIPCO 2020: The 28th European Signal Processing Conference - Amsterdam, Netherlands
Duration: 18 Jan 202122 Jan 2021
Conference number: 28th

Publication series

NameEuropean Signal Processing Conference
ISSN (Print)2219-5491


ConferenceEUSIPCO 2020
OtherDate change due to COVID-19 (former date August 24-28 2020)

Bibliographical note

Green Open Access added to TU Delft Institutional Repository ‘You share, we take care!’ – Taverne project
Otherwise as indicated in the copyright section: the publisher is the copyright holder of this work and the author uses the Dutch legislation to make this work public.


  • Graph signal processing
  • Signal processing over networks
  • State-space models
  • Topology identification


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