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A Novel Method Based on Node’s Correlation to Evaluate Important Nodes in Complex Networks

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Abstract

Finding the important nodes in complex networks by topological structure is of great significance to network invulnerability. Several centrality measures have been proposed recently to evaluate the performance of nodes based on their correlation, showing that the interaction between nodes has an influence on the importance of nodes. In this paper, a novel method based on node’s distribution and global influence in complex networks is proposed. The nodes in the complex networks are classified according to the distance matrix, then the correlation coefficient between pairs of nodes is calculated. From the whole perspective in the network, the global similarity centrality (GSC) is proposed based on the relevance and the shortest distance between any two nodes. The efficiency, accuracy, and monotonicity of the proposed method are analyzed in two artificial datasets and eight real datasets of different sizes. Experimental results show that the performance of GSC method outperforms those current state-of-the-art algorithms.

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Authors and Affiliations

  1. School of Computer and Communication, Lanzhou University of Technology, Lanzhou, 730050, China

    Pengli Lu  (卢鹏丽) & Chen Dong  (董晨)

  2. School of Management, Shanghai University, Shanghai, 200444, China

    Chen Dong  (董晨)

  3. School of Mathematics and Statistics, Hexi University, Zhangye, Gansu, 734000, China

    Yuhong Guo  (郭育红)

Authors
  1. Pengli Lu  (卢鹏丽)
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  2. Chen Dong  (董晨)
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  3. Yuhong Guo  (郭育红)
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Corresponding author

Correspondence to Pengli Lu  (卢鹏丽).

Additional information

Foundation item: the National Natural Science Foundation of China (Nos. 11361033, 62162040 and 11861045)

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Lu, P., Dong, C. & Guo, Y. A Novel Method Based on Node’s Correlation to Evaluate Important Nodes in Complex Networks. J. Shanghai Jiaotong Univ. (Sci.) 27, 688–698 (2022). https://doi.org/10.1007/s12204-021-2373-6

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  • DOI: https://doi.org/10.1007/s12204-021-2373-6

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