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Identification of highly susceptible individuals in complex networks

Author

Listed:
  • Tang, Shaoting
  • Teng, Xian
  • Pei, Sen
  • Yan, Shu
  • Zheng, Zhiming

Abstract

Identifying highly susceptible individuals in spreading processes is of great significance in controlling outbreaks. In this paper, we explore the susceptibility of people in susceptible-infectious-recovered (SIR) and rumor spreading dynamics. We first study the impact of community structure on people’s susceptibility. Although the community structure can reduce the number of infected people for same infection rate, it will not significantly affect nodes’ susceptibility. We find the susceptibility of individuals is sensitive to the choice of spreading dynamics. For SIR spreading, since the susceptibility is highly correlated to nodes’ influence, the topological indicator k-shell can better identify highly susceptible individuals, outperforming degree, betweenness centrality and PageRank. In contrast, in rumor spreading model, where nodes’ susceptibility and influence have no clear correlation, degree performs the best among considered topological measures. Our finding highlights the significance of both topological features and spreading mechanisms in identifying highly susceptible population.

Suggested Citation

  • Tang, Shaoting & Teng, Xian & Pei, Sen & Yan, Shu & Zheng, Zhiming, 2015. "Identification of highly susceptible individuals in complex networks," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 432(C), pages 363-372.
    • Handle: RePEc:eee:phsmap:v:432:y:2015:i:c:p:363-372
    DOI: 10.1016/j.physa.2015.03.046
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    References listed on IDEAS

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    1. Carol Y. Lin, 2008. "Modeling Infectious Diseases in Humans and Animals by KEELING, M. J. and ROHANI, P," Biometrics, The International Biometric Society, vol. 64(3), pages 993-993, September.
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    Cited by:

    1. Ma, Ning & Liu, Yijun & Chi, Yuxue, 2018. "Influencer discovery algorithm in a multi-relational network," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 510(C), pages 415-425.
    2. Zhu, Canshi & Wang, Xiaoyang & Zhu, Lin, 2017. "A novel method of evaluating key nodes in complex networks," Chaos, Solitons & Fractals, Elsevier, vol. 96(C), pages 43-50.

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