State-space based network topology identification

State-space based network topology identification

Coutino, Mario (TU Delft Signal Processing Systems)
Isufi, E. (TU Delft Multimedia Computing)
Maehara, T. (RIKEN; Tokyo)
Leus, G.J.T. (TU Delft Signal Processing Systems)

2020-08-01

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.

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

http://resolver.tudelft.nl/uuid:abd4ea54-a401-4f9d-9a3a-639a8a5b2596

https://doi.org/10.23919/Eusipco47968.2020.9287692

Eurasip, Amsterdam (Netherlands)

2021-08-29

978-9-0827-9705-3

28th European Signal Processing Conference, EUSIPCO 2020 - Proceedings

EUSIPCO 2020, 2021-01-18 → 2021-01-22, Amsterdam, Netherlands

European Signal Processing Conference, 2219-5491, 2021-January

Green Open Access added to TU Delft Institutional Repository ‘You share, we take care!’ – Taverne project https://www.openaccess.nl/en/you-share-we-take-care 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.

Institutional Repository

conference paper

© 2020 Mario Coutino, E. Isufi, T. Maehara, G.J.T. Leus