Structural Robustness to Noise in Consensus Networks: Impact of Average Degrees and Average Distances

Yasin Yazicioglu, Waseem Abbas, Mudassir Shabbir

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

1 Scopus citations

Abstract

We investigate how the graph topology influences the robustness to noise in undirected linear consensus networks. We consider the expected steady state population variance of states as the measure of vulnerability to noise. We quantify the structural robustness of a network by using the smallest value this measure can attain under edge weights from the unit interval. Our main result shows that the average distance between nodes and the average node degree define tight upper and lower bounds on the structural robustness. Using these bounds, we characterize the networks with different types of robustness scaling. We also present a fundamental trade-off between the structural robustness and the sparsity of networks. We then show that random regular graphs typically have near-optimal structural robustness among the graphs with same size and average degree. Some simulation results are also provided.

Original languageEnglish (US)
Title of host publication2019 IEEE 58th Conference on Decision and Control, CDC 2019
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages5444-5449
Number of pages6
ISBN (Electronic)9781728113982
DOIs
StatePublished - Dec 2019
Event58th IEEE Conference on Decision and Control, CDC 2019 - Nice, France
Duration: Dec 11 2019Dec 13 2019

Publication series

NameProceedings of the IEEE Conference on Decision and Control
Volume2019-December
ISSN (Print)0743-1546

Conference

Conference58th IEEE Conference on Decision and Control, CDC 2019
CountryFrance
CityNice
Period12/11/1912/13/19

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  • Cite this

    Yazicioglu, Y., Abbas, W., & Shabbir, M. (2019). Structural Robustness to Noise in Consensus Networks: Impact of Average Degrees and Average Distances. In 2019 IEEE 58th Conference on Decision and Control, CDC 2019 (pp. 5444-5449). [9029371] (Proceedings of the IEEE Conference on Decision and Control; Vol. 2019-December). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/CDC40024.2019.9029371