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

  • Thi Minh Dung TranEmail author
  • Alain Y. Kibangou
Regular Papers Control Theory and Applications


This paper concerns collaborative monitoring of the robustness of networks partitioned into sub-networks. 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.


Alterating direction method of multipliers (ADMM) graph Laplacian eigenvalues matrix factorization distributed optimization network robustness measures 


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© ICROS, KIEE and Springer 2019

Authors and Affiliations

  1. 1.Faculty of Electrical EngineeringThe University of Danang-University of Science and TechnologyDanang CityVietnam
  2. 2.Univ. Grenoble Alpes, CNRS, Inria, Grenoble INP, GIPSA-LabGrenobleFrance

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