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Identify influential spreaders in complex networks, the role of neighborhood

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  • Liu, Ying
  • Tang, Ming
  • Zhou, Tao
  • Do, Younghae

Abstract

Identifying the most influential spreaders is an important issue in controlling the spreading processes in complex networks. Centrality measures are used to rank node influence in a spreading dynamics. Here we propose a node influence measure based on the centrality of a node and its neighbors’ centrality, which we call the neighborhood centrality. By simulating the spreading processes in six real-world networks, we find that the neighborhood centrality greatly outperforms the basic centrality of a node such as the degree and coreness in ranking node influence and identifying the most influential spreaders. Interestingly, we discover a saturation effect in considering the neighborhood of a node, which is not the case of the larger the better. Specifically speaking, considering the 2-step neighborhood of nodes is a good choice that balances the cost and performance. If further step of neighborhood is taken into consideration, there is no obvious improvement and even decrease in the ranking performance. The saturation effect may be informative for studies that make use of the local structure of a node to determine its importance in the network.

Suggested Citation

  • Liu, Ying & Tang, Ming & Zhou, Tao & Do, Younghae, 2016. "Identify influential spreaders in complex networks, the role of neighborhood," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 452(C), pages 289-298.
    • Handle: RePEc:eee:phsmap:v:452:y:2016:i:c:p:289-298
    DOI: 10.1016/j.physa.2016.02.028
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    References listed on IDEAS

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    2. Xiaojian Ma & Yinghong Ma, 2019. "The Local Triangle Structure Centrality Method to Rank Nodes in Networks," Complexity, Hindawi, vol. 2019, pages 1-16, January.
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    5. Namtirtha, Amrita & Dutta, Animesh & Dutta, Biswanath, 2018. "Identifying influential spreaders in complex networks based on kshell hybrid method," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 499(C), pages 310-324.
    6. Wen, Tao & Jiang, Wen, 2019. "Identifying influential nodes based on fuzzy local dimension in complex networks," Chaos, Solitons & Fractals, Elsevier, vol. 119(C), pages 332-342.
    7. Lv, Zhiwei & Zhao, Nan & Xiong, Fei & Chen, Nan, 2019. "A novel measure of identifying influential nodes in complex networks," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 523(C), pages 488-497.
    8. 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.
    9. Liu, Qiang & Zhu, Yu-Xiao & Jia, Yan & Deng, Lu & Zhou, Bin & Zhu, Jun-Xing & Zou, Peng, 2018. "Leveraging local h-index to identify and rank influential spreaders in networks," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 512(C), pages 379-391.
    10. Fei, Liguo & Zhang, Qi & Deng, Yong, 2018. "Identifying influential nodes in complex networks based on the inverse-square law," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 512(C), pages 1044-1059.
    11. 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.
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    13. Zareie, Ahmad & Sheikhahmadi, Amir & Fatemi, Adel, 2017. "Influential nodes ranking in complex networks: An entropy-based approach," Chaos, Solitons & Fractals, Elsevier, vol. 104(C), pages 485-494.

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