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Identifying multiple influential spreaders based on generalized closeness centrality

Author

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  • Liu, Huan-Li
  • Ma, Chuang
  • Xiang, Bing-Bing
  • Tang, Ming
  • Zhang, Hai-Feng

Abstract

To maximize the spreading influence of multiple spreaders in complex networks, one important fact cannot be ignored: the multiple spreaders should be dispersively distributed in networks, which can effectively reduce the redundance of information spreading. For this purpose, we define a generalized closeness centrality (GCC) index by generalizing the closeness centrality index to a set of nodes. The problem converts to how to identify multiple spreaders such that an objective function has the minimal value. By comparing with the K-means clustering algorithm, we find that the optimization problem is very similar to the problem of minimizing the objective function in the K-means method. Therefore, how to find multiple nodes with the highest GCC value can be approximately solved by the K-means method. Two typical transmission dynamics—epidemic spreading process and rumor spreading process are implemented in real networks to verify the good performance of our proposed method.

Suggested Citation

  • Liu, Huan-Li & Ma, Chuang & Xiang, Bing-Bing & Tang, Ming & Zhang, Hai-Feng, 2018. "Identifying multiple influential spreaders based on generalized closeness centrality," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 492(C), pages 2237-2248.
    • Handle: RePEc:eee:phsmap:v:492:y:2018:i:c:p:2237-2248
    DOI: 10.1016/j.physa.2017.11.138
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    Citations

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    Cited by:

    1. Hajarathaiah, Koduru & Enduri, Murali Krishna & Anamalamudi, Satish, 2022. "Efficient algorithm for finding the influential nodes using local relative change of average shortest path," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 591(C).
    2. Zhang, Xiaohong & Li, Zhiying & Qian, Kai & Ren, Jianji & Luo, Junwei, 2020. "Influential node identification in a constrained greedy way," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 557(C).
    3. Lu, Peng & Deng, Liping & Liao, Hongbing, 2019. "Conditional effects of individual judgment heterogeneity in information dissemination," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 523(C), pages 335-344.
    4. Zhao, Yang & Yongquan, Yang & Jian, Ma & Lu, Angela & Xuanhua, Xu, 2024. "Policy-induced cooperative knowledge network, university-industry collaboration and firm innovation: Evidence from the Greater Bay Area," Technological Forecasting and Social Change, Elsevier, vol. 200(C).
    5. Wang, Xiaojie & Zhang, Xue & Zhao, Chengli & Yi, Dongyun, 2018. "Effectively identifying multiple influential spreaders in term of the backward–forward propagation," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 512(C), pages 404-413.
    6. Shang, Jiaxing & Wu, Hongchun & Zhou, Shangbo & Zhong, Jiang & Feng, Yong & Qiang, Baohua, 2018. "IMPC: Influence maximization based on multi-neighbor potential in community networks," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 512(C), pages 1085-1103.
    7. Wang, Ying & Zheng, Yunan & Shi, Xuelei & Liu, Yiguang, 2022. "An effective heuristic clustering algorithm for mining multiple critical nodes in complex networks," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 588(C).
    8. Zhang, Jun-li & Fu, Yan-jun & Cheng, Lan & Yang, Yun-yun, 2021. "Identifying multiple influential spreaders based on maximum connected component decomposition method," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 571(C).
    9. Lu, Peng & Yao, Qi & Lu, Pengfei, 2019. "Two-stage predictions of evolutionary dynamics during the rumor dissemination," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 517(C), pages 349-369.
    10. Lu, Peng, 2019. "Heterogeneity, judgment, and social trust of agents in rumor spreading," Applied Mathematics and Computation, Elsevier, vol. 350(C), pages 447-461.

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