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Combination methods for identifying influential nodes in networks

Author

Listed:
  • Chao Gao

    (College of Computer and Information Science, Southwest University, Chongqing 400715, P. R. China;
    Key Laboratory of Symbolic Computation and Knowledge, Engineering of Ministry of Education, Jilin University, Changchun 130012, P. R. China)

  • Lu Zhong

    (College of Computer and Information Science, Southwest University, Chongqing 400715, P. R. China)

  • Xianghua Li

    (College of Computer and Information Science, Southwest University, Chongqing 400715, P. R. China)

  • Zili Zhang

    (College of Computer and Information Science, Southwest University, Chongqing 400715, P. R. China;
    School of Information Technology, Deakin University, VIC 3217, Australia)

  • Ning Shi

    (College of Computer and Information Science, Southwest University, Chongqing 400715, P. R. China)

Abstract

Identifying influential nodes is of theoretical significance in many domains. Although lots of methods have been proposed to solve this problem, their evaluations are under single-source attack in scale-free networks. Meanwhile, some researches have speculated that the combinations of some methods may achieve more optimal results. In order to evaluate this speculation and design a universal strategy suitable for different types of networks under the consideration of multi-source attacks, this paper proposes an attribute fusion method with two independent strategies to reveal the correlation of existing ranking methods and indicators. One is based on feature union (FU) and the other is based on feature ranking (FR). Two different propagation models in the fields of recommendation system and network immunization are used to simulate the efficiency of our proposed method. Experimental results show that our method can enlarge information spreading and restrain virus propagation in the application of recommendation system and network immunization in different types of networks under the condition of multi-source attacks.

Suggested Citation

  • Chao Gao & Lu Zhong & Xianghua Li & Zili Zhang & Ning Shi, 2015. "Combination methods for identifying influential nodes in networks," International Journal of Modern Physics C (IJMPC), World Scientific Publishing Co. Pte. Ltd., vol. 26(06), pages 1-20.
    • Handle: RePEc:wsi:ijmpcx:v:26:y:2015:i:06:n:s0129183115500679
    DOI: 10.1142/S0129183115500679
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    Citations

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    Cited by:

    1. Li, Xianghua & Guo, Jingyi & Gao, Chao & Zhang, Leyan & Zhang, Zili, 2018. "A hybrid strategy for network immunization," Chaos, Solitons & Fractals, Elsevier, vol. 106(C), pages 214-219.
    2. Shen, Dongqin & Cao, Shanshan, 2018. "An efficient immunization strategy based on transmission limit in weighted complex networks," Chaos, Solitons & Fractals, Elsevier, vol. 114(C), pages 1-7.
    3. Li, Ai-Wen & Xu, Xiao-Ke & Fan, Ying, 2022. "Immunization strategies for false information spreading on signed social networks," Chaos, Solitons & Fractals, Elsevier, vol. 162(C).

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