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An effective similarity measure based on kernel spectral method for complex networks

Author

Listed:
  • Longjie Li

    (School of Information Science & Engineering, Lanzhou University, Lanzhou 730000, P. R. China)

  • Lu Wang

    (School of Information Science & Engineering, Lanzhou University, Lanzhou 730000, P. R. China)

  • Shenshen Bai

    (School of Information Science & Engineering, Lanzhou University, Lanzhou 730000, P. R. China)

  • Shiyu Fang

    (School of Information Science & Engineering, Lanzhou University, Lanzhou 730000, P. R. China)

  • Jianjun Cheng

    (School of Information Science & Engineering, Lanzhou University, Lanzhou 730000, P. R. China)

  • Xiaoyun Chen

    (School of Information Science & Engineering, Lanzhou University, Lanzhou 730000, P. R. China)

Abstract

Node similarity measure is a special important task in complex network analysis and plays a critical role in a multitude of applications, such as link prediction, community detection, and recommender systems. In this study, we are interested in link-based similarity measures, which only concern the structural information of networks when estimating node similarity. A new algorithm is proposed by adopting the idea of kernel spectral method to quantify the similarity of nodes. When computing the kernel matrix, the proposed algorithm makes use of local structural information, but it takes advantage of global information when constructing the feature matrix. Thence, the proposed algorithm could better capture potential relationships between nodes. To show the superiority of our algorithm over others, we conduct experiments on 10 real-world networks. Experimental results demonstrate that our algorithm yields more reasonable results and better performance of accuracy than baselines.

Suggested Citation

  • Longjie Li & Lu Wang & Shenshen Bai & Shiyu Fang & Jianjun Cheng & Xiaoyun Chen, 2019. "An effective similarity measure based on kernel spectral method for complex networks," International Journal of Modern Physics C (IJMPC), World Scientific Publishing Co. Pte. Ltd., vol. 30(07), pages 1-21, July.
    • Handle: RePEc:wsi:ijmpcx:v:30:y:2019:i:07:n:s0129183119400059
    DOI: 10.1142/S0129183119400059
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