Spectral identification of networks with generalized diffusive coupling

M. Gulina; A. Mauroy

doi:10.1016/j.ifacol.2022.11.101

Spectral identification of networks with generalized diffusive coupling

M. Gulina, A. Mauroy

Namur Institute for Complex Systems

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Résumé

Spectral network identification aims at inferring the eigenvalues of the Laplacian matrix of a network from measurement data. This allows to capture global information on the network structure from local measurements at a few number of nodes. In this paper, we consider the spectral network identification problem in the generalized setting of a vector-valued diffusive coupling. The feasibility of this problem is investigated and theoretical results on the properties of the associated generalized eigenvalue problem are obtained. Finally, we propose a numerical method to solve the generalized network identification problem, which relies on dynamic mode decomposition and leverages the above theoretical results.

langue originale	Anglais
Pages (de - à)	492-497
Nombre de pages	6
journal	IFAC-PapersOnLine
Volume	55
Numéro de publication	30
Les DOIs	https://doi.org/10.1016/j.ifacol.2022.11.101
Etat de la publication	Publié - 2022
Evénement	25th IFAC Symposium on Mathematical Theory of Networks and Systems, MTNS 2022 - Bayreuthl, Allemagne Durée: 12 sept. 2022 → 16 sept. 2022

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10.1016/j.ifacol.2022.11.101

1-s2.0-S2405896322027343-mainVersion finale publiée, 1,32 MBLicense: CC BY-NC-ND

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Spectral network identification with generalized diffusive coupling
Gulina, M. & Mauroy, A., 5 juil. 2022, 41st Benelux Meeting on Systems and Control : Book of Abstract. Vande Wouwer, A., Kinnaert, M., Garone, E., Dewasme, L. & Pimentel, G. A. (eds.). Presses universitaires de Mons, 1 p.
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Spectral network identification with generalized diffusive coupling
Gulina, M. & Mauroy, A., 12 sept. 2022, Proceedings of the 25th International Symposium on Mathematical Theory of Networks and Systems (MTNS 2022). 6 p.
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keywords = "diffusive coupling, network identification, spectral graph theory",

author = "M. Gulina and A. Mauroy",

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AB - Spectral network identification aims at inferring the eigenvalues of the Laplacian matrix of a network from measurement data. This allows to capture global information on the network structure from local measurements at a few number of nodes. In this paper, we consider the spectral network identification problem in the generalized setting of a vector-valued diffusive coupling. The feasibility of this problem is investigated and theoretical results on the properties of the associated generalized eigenvalue problem are obtained. Finally, we propose a numerical method to solve the generalized network identification problem, which relies on dynamic mode decomposition and leverages the above theoretical results.

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T2 - 25th IFAC Symposium on Mathematical Theory of Networks and Systems, MTNS 2022

Y2 - 12 September 2022 through 16 September 2022

ER -

Spectral identification of networks with generalized diffusive coupling

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Spectral network identification with generalized diffusive coupling

Spectral network identification with generalized diffusive coupling

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