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Efficient hybrid PageRank centrality computation for multilayer networks

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Listed:
  • Shen, Zhao-Li
  • Jiao, Yue-Hao
  • Wei, Yi-Kun
  • Wen, Chun
  • Carpentieri, Bruno

Abstract

Quantifying node centrality in multilayer networks is crucial for identifying influential nodes across various applications. Building on the PageRank model for single-layer networks, Lv et al. recently introduced a promising multilayer PageRank model for assessing node and layer centrality. In this paper, we reformulate this model within a discrete Markov chain framework. Our approach incorporates link diversity to enhance centrality measurement and ensures irreducibility within the internal Markov chains. This refinement enables an efficient computational strategy leveraging numerical algebra techniques. Experiments across diverse multilayer networks demonstrate the model’s effectiveness and computational efficiency, particularly for large-scale networks.

Suggested Citation

  • Shen, Zhao-Li & Jiao, Yue-Hao & Wei, Yi-Kun & Wen, Chun & Carpentieri, Bruno, 2025. "Efficient hybrid PageRank centrality computation for multilayer networks," Chaos, Solitons & Fractals, Elsevier, vol. 192(C).
    • Handle: RePEc:eee:chsofr:v:192:y:2025:i:c:s0960077925000311
    DOI: 10.1016/j.chaos.2025.116018
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    References listed on IDEAS

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    1. Francis Bloch & Matthew O. Jackson & Pietro Tebaldi, 2023. "Centrality measures in networks," Social Choice and Welfare, Springer;The Society for Social Choice and Welfare, vol. 61(2), pages 413-453, August.
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    5. Ma, Jinlong & Kong, Lingkang, 2023. "The influence of edge-adding strategy on traffic capacity of multilayer network," Chaos, Solitons & Fractals, Elsevier, vol. 168(C).
    6. Beheshtian-Ardakani, Arash & Salehi, Mostafa & Sharma, Rajesh, 2023. "CMPN: Modeling and analysis of soccer teams using Complex Multiplex Passing Network," Chaos, Solitons & Fractals, Elsevier, vol. 174(C).
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