Distributed Analytical Graph Identification

Sundeep Prabhakar Chepuri; Mario Coutino; Antonio G. Marques; Geert Leus

doi:10.1109/ICASSP.2018.8461484

Distributed Analytical Graph Identification

Sundeep Prabhakar Chepuri, Mario Coutino, Antonio G. Marques, Geert Leus

Signal Processing Systems

Research output: Chapter in Book/Conference proceedings/Edited volume › Conference contribution › Scientific › peer-review

1 Citation (Scopus)

27 Downloads (Pure)

Abstract

An analytical algebraic approach for distributed network identification is presented in this paper. The information propagation in the network is modeled using a state-space representation. Using the observations recorded at a single node and a known excitation signal, we present algorithms to compute the eigenfrequencies and eigenmodes of the graph in a distributed manner. The eigenfrequencies of the graph may be computed using a generalized eigenvalue algorithm, while the eigenmodes can be computed using an eigenvalue decomposition. The developed theory is demonstrated using numerical experiments.

Original language	English
Title of host publication	2018 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2018 - Proceedings
Place of Publication	Piscataway, NJ
Publisher	IEEE
Pages	4064-4068
Number of pages	5
ISBN (Electronic)	978-1-5386-4658-8
ISBN (Print)	978-1-5386-4659-5
DOIs	https://doi.org/10.1109/ICASSP.2018.8461484
Publication status	Published - 2018
Event	2018 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2018: Signal Processing and Artificial Intelligence: Changing the World - Calgary Telus Convention Center (CTCC), Calgary, Canada Duration: 15 Apr 2018 → 20 Apr 2018 https://2018.ieeeicassp.org

Conference

Conference	2018 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2018
Country/Territory	Canada
City	Calgary
Period	15/04/18 → 20/04/18
Internet address	https://2018.ieeeicassp.org

Bibliographical note

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.

Keywords

Distributed graph-spectral decomposition
Graph signal processing
Spectrum analysis
System identification
Topology identification

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10.1109/ICASSP.2018.8461484

Distributed_Analytical_Graph_IdentificationFinal published version, 296 KB

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title = "Distributed Analytical Graph Identification",

abstract = "An analytical algebraic approach for distributed network identification is presented in this paper. The information propagation in the network is modeled using a state-space representation. Using the observations recorded at a single node and a known excitation signal, we present algorithms to compute the eigenfrequencies and eigenmodes of the graph in a distributed manner. The eigenfrequencies of the graph may be computed using a generalized eigenvalue algorithm, while the eigenmodes can be computed using an eigenvalue decomposition. The developed theory is demonstrated using numerical experiments.",

keywords = "Distributed graph-spectral decomposition, Graph signal processing, Spectrum analysis, System identification, Topology identification",

author = "Chepuri, {Sundeep Prabhakar} and Mario Coutino and Marques, {Antonio G.} and Geert Leus",

note = "Green Open Access added to TU Delft Institutional Repository {\textquoteleft}You share, we take care!{\textquoteright} – 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.; 2018 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2018 : Signal Processing and Artificial Intelligence: Changing the World ; Conference date: 15-04-2018 Through 20-04-2018",

year = "2018",

doi = "10.1109/ICASSP.2018.8461484",

language = "English",

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pages = "4064--4068",

booktitle = "2018 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2018 - Proceedings",

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Chepuri, SP, Coutino, M, Marques, AG & Leus, G 2018, Distributed Analytical Graph Identification. in 2018 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2018 - Proceedings., 8461484, IEEE, Piscataway, NJ, pp. 4064-4068, 2018 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2018, Calgary, Canada, 15/04/18. https://doi.org/10.1109/ICASSP.2018.8461484

Distributed Analytical Graph Identification. / Chepuri, Sundeep Prabhakar; Coutino, Mario; Marques, Antonio G. et al.
2018 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2018 - Proceedings. Piscataway, NJ: IEEE, 2018. p. 4064-4068 8461484.

Research output: Chapter in Book/Conference proceedings/Edited volume › Conference contribution › Scientific › peer-review

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T1 - Distributed Analytical Graph Identification

AU - Chepuri, Sundeep Prabhakar

AU - Coutino, Mario

AU - Marques, Antonio G.

AU - Leus, Geert

N1 - 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.

PY - 2018

Y1 - 2018

N2 - An analytical algebraic approach for distributed network identification is presented in this paper. The information propagation in the network is modeled using a state-space representation. Using the observations recorded at a single node and a known excitation signal, we present algorithms to compute the eigenfrequencies and eigenmodes of the graph in a distributed manner. The eigenfrequencies of the graph may be computed using a generalized eigenvalue algorithm, while the eigenmodes can be computed using an eigenvalue decomposition. The developed theory is demonstrated using numerical experiments.

AB - An analytical algebraic approach for distributed network identification is presented in this paper. The information propagation in the network is modeled using a state-space representation. Using the observations recorded at a single node and a known excitation signal, we present algorithms to compute the eigenfrequencies and eigenmodes of the graph in a distributed manner. The eigenfrequencies of the graph may be computed using a generalized eigenvalue algorithm, while the eigenmodes can be computed using an eigenvalue decomposition. The developed theory is demonstrated using numerical experiments.

KW - Distributed graph-spectral decomposition

KW - Graph signal processing

KW - Spectrum analysis

KW - System identification

KW - Topology identification

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M3 - Conference contribution

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SN - 978-1-5386-4659-5

SP - 4064

EP - 4068

BT - 2018 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2018 - Proceedings

PB - IEEE

CY - Piscataway, NJ

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Y2 - 15 April 2018 through 20 April 2018

ER -

Distributed Analytical Graph Identification

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