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Identifying vital spreaders in complex networks based on the interpretative structure model and improved Kshell

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Abstract

The identification of vital spreaders in complex networks has been one of the most interesting topics in network science. Several methods were proposed to deal with this challenge, but there still exist deficiencies in previous methods, such as excessive time complexity, inadequate accuracy of recognition results after dividing the topological structure, and the ignorance of neighbors’ attribute information in the links’ significance model. To address these issues and promote identifying ability more effectively, the proposed extended centrality upon hybrid information, named EISMC, introduces the interpretative structure model (ISM) and improves hierarchical weight results after the division in hierarchies. Based on the hierarchical structure of Improved Kshell decomposition (IKs), the weight value of each layer is updated, and meanwhile the local centrality under link significance (LinkC) is created to supplement local features in this method. In this paper, six real-world networks and nine comparison methods are applied to conduct a series of simulations and tests. Results demonstrate that the proposed method outperforms state-of-the-art algorithms in the identifying effects for good spreading influence.

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Funding

This work was supported by National Natural Science Foundation of China (Grant number: 52177090); Postgraduate Research & Practice Innovation Program of Jiangsu Province of China (Grant number: KYCX23_0476).

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Authors and Affiliations

  1. School of Automation, Nanjing University of Science and Technology, 200 Xiaolingwei, Nanjing, 210094, Jiangsu Province, China

    Tianchi Tong, Qian Dong, Wenying Yuan & Jinsheng Sun

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  1. Tianchi Tong
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  2. Qian Dong
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  3. Wenying Yuan
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  4. Jinsheng Sun
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Correspondence to Jinsheng Sun.

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The authors declare the following financial interests/personal relationships which may be considered as potential competing interests: TianChi Tong reports financial support was provided by National Natural Science Foundation of China and Postgraduate Research & Practice Innovation Program of Jiangsu Province of China.

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Appendix

Appendix

Table 7 shows results of Kendall’s coefficient generated by each method and SIS in different networks in Sect. 4.4. Figure 8 represents running time costs of Cnc+, Kshell, MCDE, MDD, SCL and EISMC in six networks in Sect. 4.6.

Table 7 The correlation of different measures compared with the spreading ability is measured by Kendall’s coefficient
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Fig. 8
figure 8

Time costs of execution of different methods in six networks

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Tong, T., Dong, Q., Yuan, W. et al. Identifying vital spreaders in complex networks based on the interpretative structure model and improved Kshell. Computing 106, 1335–1358 (2024). https://doi.org/10.1007/s00607-024-01268-z

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