Collaborative Network Monitoring by Means of Laplacian Spectrum Estimation and Average Consensus

Thi Minh Dung Tran 1 Alain Kibangou 2
2 NECS-POST - Systèmes Commandés en Réseau
Inria Grenoble - Rhône-Alpes, GIPSA-DA - Département Automatique
Abstract : This paper concerns collaborative monitoring of the robustness of networks partitioned into subnetworks. We consider the critical threshold of a network and the effective graph resistance (Kirchhoff index) of a sub-graph characterizing the interconnection of sub-networks, that are partitioned from the given network as robustness metric. In which, the critical threshold depends only on the two first moments of the degree distribution while the Kirchhoff index can be computed with Laplacian eigenvalues. Therefore, we show how to estimate jointly the Laplacian eigenvalues and the two first moments of the degree distribution in a distributed way.
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https://hal.archives-ouvertes.fr/hal-02166871
Contributor : Alain Kibangou <>
Submitted on : Thursday, June 27, 2019 - 11:41:32 AM
Last modification on : Thursday, July 25, 2019 - 2:59:59 PM

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Thi Minh Dung Tran, Alain Kibangou. Collaborative Network Monitoring by Means of Laplacian Spectrum Estimation and Average Consensus. International Journal of Control, Automation and Systems, Springer, 2019, 17 (7), pp.1826-1837. ⟨10.1007/s12555-018-0638-0⟩. ⟨hal-02166871⟩

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