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Intrinsic Correlation with Betweenness Centrality and Distribution of Shortest Paths

Author

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  • Yelai Feng

    (College of Electronic Engineering, National University of Defense Technology, Hefei 230000, China
    College of Computer, National University of Defense Technology, Changsha 410000, China)

  • Huaixi Wang

    (College of Electronic Engineering, National University of Defense Technology, Hefei 230000, China)

  • Chao Chang

    (College of Electronic Engineering, National University of Defense Technology, Hefei 230000, China)

  • Hongyi Lu

    (College of Computer, National University of Defense Technology, Changsha 410000, China)

Abstract

Betweenness centrality evaluates the importance of nodes and edges in networks and is one of the most pivotal indices in complex network analysis; for example, it is widely used in centrality ordering, failure cascading modeling, and path planning. Existing algorithms are based on single-source shortest paths technology, which cannot show the change of betweenness centrality with the growth of paths, and prevents deep analysis. We propose a novel algorithm that calculates betweenness centrality hierarchically and accelerates computing via GPUs. Based on the novel algorithm, we find that the distribution of shortest path has an intrinsic correlation with betweenness centrality. Furthermore, we find that the betweenness centrality indices of some nodes are 0, but these nodes are not edge nodes, and they characterize critical significance in real networks. Experimental evidence shows that betweenness centrality is closely related to the distribution of the shortest paths.

Suggested Citation

  • Yelai Feng & Huaixi Wang & Chao Chang & Hongyi Lu, 2022. "Intrinsic Correlation with Betweenness Centrality and Distribution of Shortest Paths," Mathematics, MDPI, vol. 10(14), pages 1-18, July.
    • Handle: RePEc:gam:jmathe:v:10:y:2022:i:14:p:2521-:d:867103
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    References listed on IDEAS

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    Cited by:

    1. Alexey V. Yakovlev & Vladimir V. Alekseev & Maria V. Volchikhina & Sergey V. Petrenko, 2022. "A Combinatorial Model for Determining Information Loss in Organizational and Technical Systems," Mathematics, MDPI, vol. 10(19), pages 1-12, September.

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