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Identifying influential spreaders based on indirect spreading in neighborhood

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  • Yu, Senbin
  • Gao, Liang
  • Xu, Lida
  • Gao, Zi-You

Abstract

Identifying influential spreaders plays a crucial role in understanding and controlling the spreading processes on complex networks. Previous works mainly focus on the direct spread via edge eij from node i to node j. However, the indirect spread through an intermediate node k, i.e. the infection is spread successively via edge eik and edge ekj, is a ubiquitous phenomenon in a spreading process. Considering the spreading influence of a node, an asymmetric connection strength cij is proposed, which combines the indirect infections in i’s neighborhood with the traditional direct infections. Then node i’s spreading influence sic, is defined as the sum of cij among i’s neighbors. We investigate the Susceptible–Infected–Removed (SIR) model on nine real-world networks to evaluate the accuracy of sc in ranking the spreading influence of nodes. The results show that sc is more accurate and more robust ranking of nodes’ spreading influence in general compared with the node strength s and s-shell index ss. Our research sheds light on the mechanism that dominates the spreading strength of nodes. The indirect spread among the neighborhood effectively catches more details for ranking the node influence in the spreading process.

Suggested Citation

  • Yu, Senbin & Gao, Liang & Xu, Lida & Gao, Zi-You, 2019. "Identifying influential spreaders based on indirect spreading in neighborhood," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 523(C), pages 418-425.
    • Handle: RePEc:eee:phsmap:v:523:y:2019:i:c:p:418-425
    DOI: 10.1016/j.physa.2019.02.010
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    2. Feng, Xiao & He, Shiwei & Li, Guangye & Chi, Jushang, 2021. "Transfer network of high-speed rail and aviation: Structure and critical components," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 581(C).

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