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A novel weight neighborhood centrality algorithm for identifying influential spreaders in complex networks

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  • Wang, Junyi
  • Hou, Xiaoni
  • Li, Kezan
  • Ding, Yong

Abstract

Identifying the most influential spreaders in complex networks is crucial for optimally using the network structure and designing efficient strategies to accelerate information dissemination or prevent epidemic outbreaks. In this paper, by taking into account the centrality of a node and its neighbors’ centrality which depends on the diffusion importance of links, we propose a novel influence measure, the weight neighborhood centrality, to quantify the spreading ability of nodes in complex networks. To evaluate the performance of our method, we use the Susceptible–Infected–Recovered (SIR) model to simulate the epidemic spreading process on six real-world networks and four artificial networks. By measuring the rank imprecision and the rank correlation between the rank lists generated by simulation results via SIR and the ones generated by centrality measures, it shows that in general the weight neighborhood centrality can rank the spreading ability of nodes more accurately than its benchmark centrality, especially when using the degree k or coreness ks as the benchmark centrality. Further, we compare the monotonicity and the computational complexity of different ranking methods, which show that our method not only can be better at distinguishing the spreading ability of nodes but also can be used in large-scale networks due to the high computation efficiency.

Suggested Citation

  • Wang, Junyi & Hou, Xiaoni & Li, Kezan & Ding, Yong, 2017. "A novel weight neighborhood centrality algorithm for identifying influential spreaders in complex networks," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 475(C), pages 88-105.
  • Handle: RePEc:eee:phsmap:v:475:y:2017:i:c:p:88-105
    DOI: 10.1016/j.physa.2017.02.007
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    References listed on IDEAS

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    Cited by:

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    2. Wang, Zi-Yi & Han, Jing-Ti & Zhao, Jun, 2017. "Identifying node spreading influence for tunable clustering coefficient networks," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 486(C), pages 242-250.
    3. Wang, Min & Li, Wanchun & Guo, Yuning & Peng, Xiaoyan & Li, Yingxiang, 2020. "Identifying influential spreaders in complex networks based on improved k-shell method," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 554(C).
    4. Dong, Chen & Xu, Guiqiong & Meng, Lei & Yang, Pingle, 2022. "CPR-TOPSIS: A novel algorithm for finding influential nodes in complex networks based on communication probability and relative entropy," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 603(C).
    5. Wang, Anqun & Chen, Jun & Wang, Li & Han, Junlei & Su, Weiguang & Li, Anqing & Liu, Pengbo & Duan, Liya & Xu, Chonghai & Zeng, Zheng, 2022. "Numerical analysis and experimental study of an ocean wave tetrahedral triboelectric nanogenerator," Applied Energy, Elsevier, vol. 307(C).

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