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A centrality measure for quantifying spread on weighted, directed networks

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  • Fink, Christian G.
  • Fullin, Kelly
  • Gutierrez, Guillermo
  • Omodt, Nathan
  • Zinnecker, Sydney
  • Sprint, Gina
  • McCulloch, Sean

Abstract

While many centrality measures for complex networks have been proposed, relatively few have been developed specifically for weighted, directed (WD) networks. Here we propose a centrality measure (Viral Centrality) for spread (of information, pathogens, etc.) through WD networks based on the independent cascade model (ICM). While calculating the most accurate results for the ICM generally requires Monte Carlo simulations, we show that Viral Centrality provides excellent approximation to ICM results for networks in which the weighted strength of cycles is not too large. We show this can be quantified with the leading eigenvalue of the weighted adjacency matrix, and we show that Viral Centrality outperforms other common centrality measures in both simulated and empirical WD networks. A Python implementation of the Viral Centrality algorithm has been made available at the Stanford Network Analysis Project repository.

Suggested Citation

  • Fink, Christian G. & Fullin, Kelly & Gutierrez, Guillermo & Omodt, Nathan & Zinnecker, Sydney & Sprint, Gina & McCulloch, Sean, 2023. "A centrality measure for quantifying spread on weighted, directed networks," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 626(C).
    • Handle: RePEc:eee:phsmap:v:626:y:2023:i:c:s0378437123006386
    DOI: 10.1016/j.physa.2023.129083
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    References listed on IDEAS

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    1. Byungjoon Min, 2018. "Identifying an influential spreader from a single seed in complex networks via a message-passing approach," The European Physical Journal B: Condensed Matter and Complex Systems, Springer;EDP Sciences, vol. 91(1), pages 1-6, January.
    2. Wei, Bo & Liu, Jie & Wei, Daijun & Gao, Cai & Deng, Yong, 2015. "Weighted k-shell decomposition for complex networks based on potential edge weights," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 420(C), pages 277-283.
    3. Zhang, Panpan & Wang, Tiandong & Yan, Jun, 2022. "PageRank centrality and algorithms for weighted, directed networks," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 586(C).
    4. Flaviano Morone & Hernán A. Makse, 2015. "Correction: Corrigendum: Influence maximization in complex networks through optimal percolation," Nature, Nature, vol. 527(7579), pages 544-544, November.
    5. Flaviano Morone & Hernán A. Makse, 2015. "Influence maximization in complex networks through optimal percolation," Nature, Nature, vol. 524(7563), pages 65-68, August.
    6. Nekovee, M. & Moreno, Y. & Bianconi, G. & Marsili, M., 2007. "Theory of rumour spreading in complex social networks," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 374(1), pages 457-470.
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    Cited by:

    1. Wang, Yan & Zhang, Ling & Yang, Junwen & Yan, Ming & Li, Haozhan, 2024. "Multi-factor information matrix: A directed weighted method to identify influential nodes in social networks," Chaos, Solitons & Fractals, Elsevier, vol. 180(C).

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