Distributed Analytical Graph Identification

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

Research output: Chapter in Book/Report/Conference proceedingConference contribution

1 Scopus citations


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 languageEnglish (US)
Title of host publication2018 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)
Number of pages5
ISBN (Print)9781538646588
StatePublished - Sep 13 2018
Externally publishedYes

Bibliographical note

KAUST Repository Item: Exported on 2022-06-30
Acknowledged KAUST grant number(s): OSR-2015-Sensors-2700
Acknowledgements: This work is supported in part by the KAUST-MIT-TUD consortium under grant OSR-2015-Sensors-2700, the ASPIRE project (project 14926 within the STW OTP program), and MINECO (grants TEC2013-41604-R and TEC2016-75361-R). Mario Coutino is partially supported by CONA-CYT.
This publication acknowledges KAUST support, but has no KAUST affiliated authors.


Dive into the research topics of 'Distributed Analytical Graph Identification'. Together they form a unique fingerprint.

Cite this