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