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A novel method to identify influential nodes in complex networks

Author

Listed:
  • Yuanzhi Yang

    (Aeronautics Engineering College, Air Force Engineering University, Xi’an 710038, P. R. China)

  • Lei Yu

    (Aeronautics Engineering College, Air Force Engineering University, Xi’an 710038, P. R. China)

  • Xing Wang

    (Aeronautics Engineering College, Air Force Engineering University, Xi’an 710038, P. R. China)

  • Siyi Chen

    (Aeronautics Engineering College, Air Force Engineering University, Xi’an 710038, P. R. China)

  • You Chen

    (Aeronautics Engineering College, Air Force Engineering University, Xi’an 710038, P. R. China)

  • Yipeng Zhou

    (Aeronautics Engineering College, Air Force Engineering University, Xi’an 710038, P. R. China)

Abstract

Identifying influential nodes in complex networks continues to be an open and vital issue, which is of great significance to the robustness and vulnerability of networks. In order to accurately identify influential nodes in complex networks and avoid the deviation in the evaluation of node influence by single measure, a novel method based on improved Technology for Order Preference by Similarity to an Ideal Solution (TOPSIS) is proposed to integrate multiple measures and identify influential nodes. Our method takes into account degree centrality (DC), closeness centrality (CC) and betweenness centrality (BC), and uses the information of the decision matrix to objectively assign weight to each measure, and takes the closeness degree from each node to be the ideal solution as the basis for comprehensive evaluation. At last, four experiments based on the Susceptible-Infected (SI) model are carried out, and the superiority of our method can be demonstrated.

Suggested Citation

  • Yuanzhi Yang & Lei Yu & Xing Wang & Siyi Chen & You Chen & Yipeng Zhou, 2020. "A novel method to identify influential nodes in complex networks," International Journal of Modern Physics C (IJMPC), World Scientific Publishing Co. Pte. Ltd., vol. 31(02), pages 1-14, February.
    • Handle: RePEc:wsi:ijmpcx:v:31:y:2020:i:02:n:s0129183120500229
    DOI: 10.1142/S0129183120500229
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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. Wang, Yan & Li, Haozhan & Zhang, Ling & Zhao, Linlin & Li, Wanlan, 2022. "Identifying influential nodes in social networks: Centripetal centrality and seed exclusion approach," Chaos, Solitons & Fractals, Elsevier, vol. 162(C).
    3. Jin, Pengfei & Wang, Saige & Meng, Zheng & Chen, Bin, 2023. "China's lithium supply chains: Network evolution and resilience assessment," Resources Policy, Elsevier, vol. 87(PB).
    4. Zhang, Xian-Jie & Wang, Jing & Ma, Xiao-Jing & Ma, Chuang & Kan, Jia-Qian & Zhang, Hai-Feng, 2022. "Influence maximization in social networks with privacy protection," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 607(C).
    5. Liu, Jia-Bao & Zheng, Ya-Qian & Lee, Chien-Chiang, 2024. "Statistical analysis of the regional air quality index of Yangtze River Delta based on complex network theory," Applied Energy, Elsevier, vol. 357(C).
    6. Wu, Zhaoyan, 2024. "Intermittent control for identifying network topology," Chaos, Solitons & Fractals, Elsevier, vol. 179(C).
    7. Du, Yuxian & Lin, Xi & Pan, Ye & Chen, Zhaoxin & Xia, Huan & Luo, Qian, 2023. "Identifying influential airports in airline network based on failure risk factors with TOPSIS," Chaos, Solitons & Fractals, Elsevier, vol. 169(C).
    8. Xiao, Feng & Li, Jin & Wei, Bo, 2022. "Cascading failure analysis and critical node identification in complex networks," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 596(C).
    9. Cao, Huiying & Gao, Chao & Wang, Zhen, 2023. "Ranking academic institutions by means of institution–publication networks," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 629(C).
    10. Xu, Guiqiong & Meng, Lei, 2023. "A novel algorithm for identifying influential nodes in complex networks based on local propagation probability model," Chaos, Solitons & Fractals, Elsevier, vol. 168(C).
    11. Wu, Yali & Dong, Ang & Ren, Yuanguang & Jiang, Qiaoyong, 2023. "Identify influential nodes in complex networks: A k-orders entropy-based method," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 632(P1).
    12. 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).
    13. Xinyu Huang & Dongming Chen & Dongqi Wang & Tao Ren, 2020. "MINE: Identifying Top- k Vital Nodes in Complex Networks via Maximum Influential Neighbors Expansion," Mathematics, MDPI, vol. 8(9), pages 1-25, August.
    14. Liu, Panfeng & Li, Longjie & Fang, Shiyu & Yao, Yukai, 2021. "Identifying influential nodes in social networks: A voting approach," Chaos, Solitons & Fractals, Elsevier, vol. 152(C).
    15. Chaharborj, Sarkhosh Seddighi & Nabi, Khondoker Nazmoon & Feng, Koo Lee & Chaharborj, Shahriar Seddighi & Phang, Pei See, 2022. "Controlling COVID-19 transmission with isolation of influential nodes," Chaos, Solitons & Fractals, Elsevier, vol. 159(C).
    16. Lei, Mingli & Cheong, Kang Hao, 2022. "Node influence ranking in complex networks: A local structure entropy approach," Chaos, Solitons & Fractals, Elsevier, vol. 160(C).
    17. Li, Qi & Cheng, Le & Wang, Wei & Li, Xianghua & Li, Shudong & Zhu, Peican, 2023. "Influence maximization through exploring structural information," Applied Mathematics and Computation, Elsevier, vol. 442(C).
    18. Wang, Longjian & Zheng, Shaoya & Wang, Yonggang & Wang, Longfei, 2021. "Identification of critical nodes in multimodal transportation network," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 580(C).

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