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Identification of Influential Nodes via Effective Distance-based Centrality Mechanism in Complex Networks

Author

Listed:
  • Aman Ullah
  • Bin wang
  • Jinfang Sheng
  • Jun Long
  • Nasrullah Khan
  • Lucia Valentina Gambuzza

Abstract

Efficient identification of influential nodes is one of the essential aspects in the field of complex networks, which has excellent theoretical and practical significance in the real world. A valuable number of approaches have been developed and deployed in these areas where just a few have used centrality measures along with their concerning deficiencies and limitations in their studies. Therefore, to resolve these challenging issues, we propose a novel effective distance-based centrality (EDBC) algorithm for the identification of influential nodes in concerning networks. EDBC algorithm comprises factors such as the power of K-shell, degree nodes, effective distance, and numerous levels of neighbor’s influence or neighborhood potential. The performance of the proposed algorithm is evaluated on nine real-world networks, where a susceptible infected recovered (SIR) epidemic model is employed to examine the spreading dynamics of each node. Simulation results demonstrate that the proposed algorithm outperforms the existing techniques such as eigenvector, betweenness, closeness centralities, hyperlink-induced topic search, H-index, K-shell, page rank, profit leader, and gravity over a valuable margin.

Suggested Citation

  • Aman Ullah & Bin wang & Jinfang Sheng & Jun Long & Nasrullah Khan & Lucia Valentina Gambuzza, 2021. "Identification of Influential Nodes via Effective Distance-based Centrality Mechanism in Complex Networks," Complexity, Hindawi, vol. 2021, pages 1-16, February.
  • Handle: RePEc:hin:complx:8403738
    DOI: 10.1155/2021/8403738
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    Cited by:

    1. Huang, Binchao & Yang, Jin-Xuan & Li, Xin, 2021. "Identifying influential links to control spreading of epidemics," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 583(C).
    2. Gao, Xin & Ye, Yunxia & Su, Wenxin & Chen, Linyan, 2023. "Assessing the comprehensive importance of power grid nodes based on DEA," International Journal of Critical Infrastructure Protection, Elsevier, vol. 42(C).

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