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A new method to identify influential nodes based on relative entropy

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  • Fei, Liguo
  • Deng, Yong

Abstract

How to identify influential nodes is still an open and vital issue in complex networks. To address this problem, a lot of centrality measures have been developed, however, only single measure is focused on by the existing studies, which has its own shortcomings. In this paper, a novel method is proposed to identify influential nodes using relative entropy and TOPSIS method, which combines the advantages of existing centrality measures. Because information flow spreads in different ways in different networks. In the specific network, the appropriate centrality measures should be considered to sort the nodes. In addition, the remoteness between the alternative and the positive/negetive ideal solution is redefined based on relative entropy, which is proven to be more effective in TOPSIS method. To demonstrate the effectiveness of the proposed method, four real networks are selected to conduct several experiments for identifying influential nodes, and the advantages of the method can be illustrated based on the experimental results.

Suggested Citation

  • Fei, Liguo & Deng, Yong, 2017. "A new method to identify influential nodes based on relative entropy," Chaos, Solitons & Fractals, Elsevier, vol. 104(C), pages 257-267.
  • Handle: RePEc:eee:chsofr:v:104:y:2017:i:c:p:257-267
    DOI: 10.1016/j.chaos.2017.08.010
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    as
    1. Deng, Xinyang & Liu, Qi & Deng, Yong, 2016. "Matrix games with payoffs of belief structures," Applied Mathematics and Computation, Elsevier, vol. 273(C), pages 868-879.
    2. Stefania Vitali & James B Glattfelder & Stefano Battiston, 2011. "The Network of Global Corporate Control," PLOS ONE, Public Library of Science, vol. 6(10), pages 1-6, October.
    3. Linyuan Lü & Yi-Cheng Zhang & Chi Ho Yeung & Tao Zhou, 2011. "Leaders in Social Networks, the Delicious Case," PLOS ONE, Public Library of Science, vol. 6(6), pages 1-9, June.
    4. Du, Yuxian & Gao, Cai & Hu, Yong & Mahadevan, Sankaran & Deng, Yong, 2014. "A new method of identifying influential nodes in complex networks based on TOPSIS," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 399(C), pages 57-69.
    5. Lee, Yan-Li & Zhou, Tao, 2017. "Fast asynchronous updating algorithms for k-shell indices," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 482(C), pages 524-531.
    6. Li, Chenzhao & Mahadevan, Sankaran, 2016. "Role of calibration, validation, and relevance in multi-level uncertainty integration," Reliability Engineering and System Safety, Elsevier, vol. 148(C), pages 32-43.
    7. Gómez, Daniel & Figueira, José Rui & Eusébio, Augusto, 2013. "Modeling centrality measures in social network analysis using bi-criteria network flow optimization problems," European Journal of Operational Research, Elsevier, vol. 226(2), pages 354-365.
    8. Gao, Cai & Wei, Daijun & Hu, Yong & Mahadevan, Sankaran & Deng, Yong, 2013. "A modified evidential methodology of identifying influential nodes in weighted networks," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 392(21), pages 5490-5500.
    9. Cai Gao & Xin Lan & Xiaoge Zhang & Yong Deng, 2013. "A Bio-Inspired Methodology of Identifying Influential Nodes in Complex Networks," PLOS ONE, Public Library of Science, vol. 8(6), pages 1-11, June.
    10. Hui-Jia Li & Huiying Li & Chuanliang Jia, 2015. "A novel dynamics combination model reveals the hidden information of community structure," International Journal of Modern Physics C (IJMPC), World Scientific Publishing Co. Pte. Ltd., vol. 26(04), pages 1-13.
    11. Xiaoge Zhang & Felix T.S. Chan & Andrew Adamatzky & Sankaran Mahadevan & Hai Yang & Zili Zhang & Yong Deng, 2017. "An intelligent physarum solver for supply chain network design under profit maximization and oligopolistic competition," International Journal of Production Research, Taylor & Francis Journals, vol. 55(1), pages 244-263, January.
    12. Lü, Linyuan & Zhou, Tao, 2011. "Link prediction in complex networks: A survey," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 390(6), pages 1150-1170.
    13. Shen, Chen & Lu, Jun & Shi, Lei, 2016. "Does coevolution setup promote cooperation in spatial prisoner's dilemma game?," Applied Mathematics and Computation, Elsevier, vol. 290(C), pages 201-207.
    14. Wei, Daijun & Deng, Xinyang & Zhang, Xiaoge & Deng, Yong & Mahadevan, Sankaran, 2013. "Identifying influential nodes in weighted networks based on evidence theory," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 392(10), pages 2564-2575.
    15. Bai, Wen-Jie & Zhou, Tao & Wang, Bing-Hong, 2007. "Immunization of susceptible–infected model on scale-free networks," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 384(2), pages 656-662.
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