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A New K-Shell Decomposition Method for Identifying Influential Spreaders of Epidemics on Community Networks

Author

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  • Gong Kai
  • Kang Li

    (School of Economic Information Engineering, Southwestern University of Finance and Economics, Chengdu, 611130, China)

Abstract

An efficient method for the identification of influential spreaders that could be used to control epidemics within populations would be of considerable importance. Generally, populations are characterized by its community structures and by the heterogeneous distributions of out-leaving links among nodes bridging over communities. A new method for community networks capable of identifying influential spreaders that accelerate the spread of disease is here proposed. In this method, influential spreaders serve as target nodes. This is based on the idea that, in k-shell decomposition method, out-leaving links and inner links are processed separately. The method was used on empirical networks constructed from online social networks, and results indicated that this method is more accurate. Its effectiveness stems from the patterns of connectivity among neighbors, and it successfully identified the important nodes. In addition, the performance of the method remained robust even when there were errors in the structure of the network.

Suggested Citation

  • Gong Kai & Kang Li, 2018. "A New K-Shell Decomposition Method for Identifying Influential Spreaders of Epidemics on Community Networks," Journal of Systems Science and Information, De Gruyter, vol. 6(4), pages 366-375, August.
    • Handle: RePEc:bpj:jossai:v:6:y:2018:i:4:p:366-375:n:6
    DOI: 10.21078/JSSI-2018-366-10
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    References listed on IDEAS

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