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Estimating user influence ranking in independent cascade model

Author

Listed:
  • Li, Pei
  • Liu, Ke
  • Li, Keqin
  • Liu, Jianxun
  • Zhou, Dong

Abstract

Nowadays, hundreds of millions of people use social networks to express their opinions and communicate with their friends. It is of importance to model and estimate the user influence in social networks. Since most studies perform Monte Carlo simulation to evaluate the user influence in the independent cascade model, which leads to tremendous computational costs, we introduce a duplicate forwarding model to characterize the diffusion process in social networks, and analyze the user influences below and above the diffusion threshold theoretically. After getting the user influence ranking, we propose a Spearman-like correlation coefficient to measure the correlation between two rankings, and find the analysis results from the duplicate forwarding model achieve much better accuracy than the measurements degree, betweenness, k-core and PageRank in estimating the user influence ranking in the independent cascade model. This approach can provide insights in modeling and estimating the influences of social network users, and can be easily extended to estimate the influence ranking for different seed sets in the problem of influence maximization.

Suggested Citation

  • Li, Pei & Liu, Ke & Li, Keqin & Liu, Jianxun & Zhou, Dong, 2021. "Estimating user influence ranking in independent cascade model," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 565(C).
    • Handle: RePEc:eee:phsmap:v:565:y:2021:i:c:s0378437120308827
    DOI: 10.1016/j.physa.2020.125584
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    References listed on IDEAS

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    1. Li, Pei & Yu, Jianyong & Liu, Jianxun & Zhou, Dong & Cao, Buqing, 2020. "Generating weighted social networks using multigraph," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 539(C).
    2. Yao, Weiyi & Jiao, Pengfei & Wang, Wenjun & Sun, Yueheng, 2019. "Understanding human reposting patterns on Sina Weibo from a global perspective," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 518(C), pages 374-383.
    3. Cristopher Moore & M. E. J. Newman, 2000. "Epidemics and Percolation in Small-World Networks," Working Papers 00-01-002, Santa Fe Institute.
    4. Jianxin Tang & Ruisheng Zhang & Yabing Yao & Zhili Zhao & Baoqiang Chai & Huan Li, 2019. "An adaptive discrete particle swarm optimization for influence maximization based on network community structure," International Journal of Modern Physics C (IJMPC), World Scientific Publishing Co. Pte. Ltd., vol. 30(06), pages 1-21, June.
    5. Qingliang Wang & Fred Miao & Giri Kumar Tayi & En Xie, 2019. "What makes online content viral? The contingent effects of hub users versus non–hub users on social media platforms," Journal of the Academy of Marketing Science, Springer, vol. 47(6), pages 1005-1026, November.
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    Cited by:

    1. Yiming Liu & Longxin Wang & Yunsong Jia & Ziwen Li & Hongju Gao, 2021. "Dynamic Influence Ranking Algorithm Based on Musicians’ Social and Personal Information Network," Mathematics, MDPI, vol. 9(20), pages 1-19, October.
    2. Wu, Rui-Jie & Kong, Yi-Xiu & Di, Zengru & Zhang, Yi-Cheng & Shi, Gui-Yuan, 2022. "Analytical solution to the k-core pruning process," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 608(P1).

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