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Sequential seeding strategy for social influence diffusion with improved entropy-based centrality

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  • Ni, Chengzhang
  • Yang, Jun
  • Kong, Demei

Abstract

In this paper, we investigate the centrality problem of selecting seed targets for sequential seeding strategy in social networks. Based on the concept of entropy, we design a novel improved centrality by integrating interaction intimacy and confidence level to measure the total influence of an individual which can be decomposed into direct effect and indirect effect. In addition, we formulate the sequential seeding strategy to evaluate the performance of the proposed centrality and compare it with the counterpart of the single-stage seeding strategy. Furthermore, extensive experiments are conducted for comparison with the other centralities including betweenness, closeness, degree, and eigenvector in two empirical and four artificial social networks. By simulations, we find that the proposed entropy-based centrality is superior to other centralities in terms of diffusion speed and influence coverage in the BA scale-free network. Parameter analysis of sequential seeding strategy demonstrates that the proposed centrality can achieve the greatest total influence coverage in the case where the individual’s confidence in each neighbor is treated equally.

Suggested Citation

  • Ni, Chengzhang & Yang, Jun & Kong, Demei, 2020. "Sequential seeding strategy for social influence diffusion with improved entropy-based centrality," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 545(C).
    • Handle: RePEc:eee:phsmap:v:545:y:2020:i:c:s0378437119320412
    DOI: 10.1016/j.physa.2019.123659
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    as
    1. Cao, Shujuan & Dehmer, Matthias, 2015. "Degree-based entropies of networks revisited," Applied Mathematics and Computation, Elsevier, vol. 261(C), pages 141-147.
    2. Liu, Jian-Guo & Ren, Zhuo-Ming & Guo, Qiang, 2013. "Ranking the spreading influence in complex networks," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 392(18), pages 4154-4159.
    3. Nie, Tingyuan & Guo, Zheng & Zhao, Kun & Lu, Zhe-Ming, 2016. "Using mapping entropy to identify node centrality in complex networks," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 453(C), pages 290-297.
    4. Linyuan Lü & Tao Zhou & Qian-Ming Zhang & H. Eugene Stanley, 2016. "The H-index of a network node and its relation to degree and coreness," Nature Communications, Nature, vol. 7(1), pages 1-7, April.
    5. 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.
    6. Duan-Bing Chen & Hui Gao & Linyuan Lü & Tao Zhou, 2013. "Identifying Influential Nodes in Large-Scale Directed Networks: The Role of Clustering," PLOS ONE, Public Library of Science, vol. 8(10), pages 1-10, October.
    7. Vijay Mahajan & Eitan Muller & Roger A. Kerin, 1984. "Introduction Strategy for New Products with Positive and Negative Word-of-Mouth," Management Science, INFORMS, vol. 30(12), pages 1389-1404, December.
    8. 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.
    9. Chen, Duanbing & Lü, Linyuan & Shang, Ming-Sheng & Zhang, Yi-Cheng & Zhou, Tao, 2012. "Identifying influential nodes in complex networks," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 391(4), pages 1777-1787.
    10. Byungjoon Min & Fredrik Liljeros & Hernán A Makse, 2015. "Finding Influential Spreaders from Human Activity beyond Network Location," PLOS ONE, Public Library of Science, vol. 10(8), pages 1-13, August.
    11. 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.
    12. Jing Zhao & Ting-Hong Yang & Yongxu Huang & Petter Holme, 2011. "Ranking Candidate Disease Genes from Gene Expression and Protein Interaction: A Katz-Centrality Based Approach," PLOS ONE, Public Library of Science, vol. 6(9), pages 1-9, September.
    13. Hinz, Oliver & Skiera, Bernd & Barrot, Christian & Becker, Jan, 2011. "Seeding Strategies for Viral Marketing: An Empirical Comparison," Publications of Darmstadt Technical University, Institute for Business Studies (BWL) 56543, Darmstadt Technical University, Department of Business Administration, Economics and Law, Institute for Business Studies (BWL).
    14. Yubo Chen & Jinhong Xie, 2008. "Online Consumer Review: Word-of-Mouth as a New Element of Marketing Communication Mix," Management Science, INFORMS, vol. 54(3), pages 477-491, March.
    15. Liu, Qipeng & Hong, Tao, 2018. "Sequential seeding for spreading in complex networks: Influence of the network topology," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 508(C), pages 10-17.
    16. Chen, Zengqiang & Dehmer, Matthias & Shi, Yongtang, 2015. "Bounds for degree-based network entropies," Applied Mathematics and Computation, Elsevier, vol. 265(C), pages 983-993.
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