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A novel semi local measure of identifying influential nodes in complex networks

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  • Wang, Xiaojie
  • Slamu, Wushour
  • Guo, Wenqiang
  • Wang, Sixiu
  • Ren, Yan

Abstract

How to identify influencers is very significance in mastering the nature of node, controlling spreading process in complex networks. In this research field, each method has its own advantages and limitations. For example, local metrics are relatively simple, global metrics can give better results, but the computational complexity is also relatively high. A semi-local approach on basis of node dimension is proposed to identify influencers. The node dimension can detect regions with different dimensional structures by scaling the local dimension on the scale. The saturation effect is discovered in the process of identifying influencers by the node dimension. When the maximum dimension radius is close to the mean shortest path length of networks, the method has better performance. Through the saturation effect, our approach can be a tradeoff between local and global metrics. In addition, we show the correlation between different measures and node dimension with different maximum dimension radii. We employ Susceptible-Infected-Recovered (SIR) model to verify the effectiveness of our designed approach. Simulation results indicate the superiority of our algorithm.

Suggested Citation

  • Wang, Xiaojie & Slamu, Wushour & Guo, Wenqiang & Wang, Sixiu & Ren, Yan, 2022. "A novel semi local measure of identifying influential nodes in complex networks," Chaos, Solitons & Fractals, Elsevier, vol. 158(C).
    • Handle: RePEc:eee:chsofr:v:158:y:2022:i:c:s0960077922002478
    DOI: 10.1016/j.chaos.2022.112037
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    References listed on IDEAS

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    1. Gupta, Naveen & Singh, Anurag & Cherifi, Hocine, 2016. "Centrality measures for networks with community structure," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 452(C), pages 46-59.
    2. Costa, Luciano da Fontoura & Rodrigues Tognetti, Marilza A. & Silva, Filipi Nascimento, 2008. "Concentric characterization and classification of complex network nodes: Application to an institutional collaboration network," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 387(24), pages 6201-6214.
    3. M. T. Gastner & M. E.J. Newman, 2006. "The spatial structure of networks," The European Physical Journal B: Condensed Matter and Complex Systems, Springer;EDP Sciences, vol. 49(2), pages 247-252, January.
    4. Duncan J. Watts & Steven H. Strogatz, 1998. "Collective dynamics of ‘small-world’ networks," Nature, Nature, vol. 393(6684), pages 440-442, June.
    5. Fei, Liguo & Zhang, Qi & Deng, Yong, 2018. "Identifying influential nodes in complex networks based on the inverse-square law," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 512(C), pages 1044-1059.
    6. Zareie, Ahmad & Sheikhahmadi, Amir & Fatemi, Adel, 2017. "Influential nodes ranking in complex networks: An entropy-based approach," Chaos, Solitons & Fractals, Elsevier, vol. 104(C), pages 485-494.
    7. Bae, Joonhyun & Kim, Sangwook, 2014. "Identifying and ranking influential spreaders in complex networks by neighborhood coreness," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 395(C), pages 549-559.
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    Cited by:

    1. Nikougoftar, Elaheh, 2024. "Strategic node identification in complex network dynamics," Chaos, Solitons & Fractals, Elsevier, vol. 187(C).

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