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Identifying influential nodes for the networks with community structure

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  • Zhao, Zi-Juan
  • Guo, Qiang
  • Yu, Kai
  • Liu, Jian-Guo

Abstract

Identifying influential nodes in complex network with community structures attracts lots of attention recently. In this paper, by combining the community structure identification method and the closeness of the network, we present a method for identifying the influential nodes. Comparing with the Susceptible–Infected–Recovered (SIR) model results on synthetic and empirical networks, the accuracy results measured by kendall’s tau τ and spearman ρ indicate that our method outperforms the existing methods to identify influential nodes for networks. The largest improved ratios η for the kendall’s tau τ and spearman ρ reach 161.6% and 157.6%.

Suggested Citation

  • Zhao, Zi-Juan & Guo, Qiang & Yu, Kai & Liu, Jian-Guo, 2020. "Identifying influential nodes for the networks with community structure," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 551(C).
    • Handle: RePEc:eee:phsmap:v:551:y:2020:i:c:s0378437119321612
    DOI: 10.1016/j.physa.2019.123893
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    References listed on IDEAS

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    1. Gert Sabidussi, 1966. "The centrality index of a graph," Psychometrika, Springer;The Psychometric Society, vol. 31(4), pages 581-603, December.
    2. Yang, Kai & Guo, Qiang & Liu, Jian-Guo, 2018. "Community detection via measuring the strength between nodes for dynamic networks," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 509(C), pages 256-264.
    3. Dangalchev, Chavdar, 2006. "Residual closeness in networks," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 365(2), pages 556-564.
    4. Pan, Ying & Li, De-Hua & Liu, Jian-Guo & Liang, Jing-Zhang, 2010. "Detecting community structure in complex networks via node similarity," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 389(14), pages 2849-2857.
    5. Li-Ying Tang & Sheng-Nan Li & Jian-Hong Lin & Qiang Guo & Jian-Guo Liu, 2016. "Community structure detection based on the neighbor node degree information," International Journal of Modern Physics C (IJMPC), World Scientific Publishing Co. Pte. Ltd., vol. 27(04), pages 1-11, April.
    6. Liu, Jian & Liu, Tingzhan, 2010. "Detecting community structure in complex networks using simulated annealing with k-means algorithms," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 389(11), pages 2300-2309.
    7. Li, Yafang & Jia, Caiyan & Yu, Jian, 2015. "A parameter-free community detection method based on centrality and dispersion of nodes in complex networks," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 438(C), pages 321-334.
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    Cited by:

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