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Distributed Average Consensus Algorithms in d-Regular Bipartite Graphs: Comparative Study

Author

Listed:
  • Martin Kenyeres

    (Institute of Informatics, Slovak Academy of Sciences, Dúbravská Cesta 9, 845 07 Bratislava, Slovakia)

  • Jozef Kenyeres

    (Frequentis AG, Innovationsstraße 1, 1100 Vienna, Austria)

Abstract

Consensus-based data aggregation in d -regular bipartite graphs poses a challenging task for the scientific community since some of these algorithms diverge in this critical graph topology. Nevertheless, one can see a lack of scientific studies dealing with this topic in the literature. Motivated by our recent research concerned with this issue, we provide a comparative study of frequently applied consensus algorithms for distributed averaging in d -regular bipartite graphs in this paper. More specifically, we examine the performance of these algorithms with bounded execution in this topology in order to identify which algorithm can achieve the consensus despite no reconfiguration and find the best-performing algorithm in these graphs. In the experimental part, we apply the number of iterations required for consensus to evaluate the performance of the algorithms in randomly generated regular bipartite graphs with various connectivities and for three configurations of the applied stopping criterion, allowing us to identify the optimal distributed consensus algorithm for this graph topology. Moreover, the obtained experimental results presented in this paper are compared to other scientific manuscripts where the analyzed algorithms are examined in non-regular non-bipartite topologies.

Suggested Citation

  • Martin Kenyeres & Jozef Kenyeres, 2023. "Distributed Average Consensus Algorithms in d-Regular Bipartite Graphs: Comparative Study," Future Internet, MDPI, vol. 15(5), pages 1-24, May.
    • Handle: RePEc:gam:jftint:v:15:y:2023:i:5:p:183-:d:1148578
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    References listed on IDEAS

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    1. Stefano Guarino & Enrico Mastrostefano & Massimo Bernaschi & Alessandro Celestini & Marco Cianfriglia & Davide Torre & Lena Rebecca Zastrow, 2021. "Inferring Urban Social Networks from Publicly Available Data," Future Internet, MDPI, vol. 13(5), pages 1-45, April.
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    4. Wei Gao & Muhammad Aamir & Zahid Iqbal & Muhammad Ishaq & Adnan Aslam, 2019. "On Irregularity Measures of Some Dendrimers Structures," Mathematics, MDPI, vol. 7(3), pages 1-15, March.
    5. Oscar Claveria, 2019. "A new consensus-based unemployment indicator," Applied Economics Letters, Taylor & Francis Journals, vol. 26(10), pages 812-817, June.
    6. E. B. Priyanka & S. Thangavel & K. Martin Sagayam & Ahmed A. Elngar, 2022. "Wireless network upgraded with artificial intelligence on the data aggregation towards the smart internet applications," International Journal of System Assurance Engineering and Management, Springer;The Society for Reliability, Engineering Quality and Operations Management (SREQOM),India, and Division of Operation and Maintenance, Lulea University of Technology, Sweden, vol. 13(3), pages 1254-1267, June.
    7. Xintao Ma & Liyan Dong & Yuequn Wang & Yongli Li & Minghui Sun, 2020. "AIRC: Attentive Implicit Relation Recommendation Incorporating Content Information for Bipartite Graphs," Mathematics, MDPI, vol. 8(12), pages 1-19, November.
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