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Complex Systems and Complexity Science >

2023 , Vol. 20 >Issue 1: 9 - 17

DOI: https://doi.org/10.13306/j.1672-3813.2023.01.002

Link Prediction Method Based on Optimal Path Similarity Transfer Matrix

  • LI Qiaoli ,
  • HAN Hua ,
  • LI Qiuhui ,
  • ZENG Xi ,
  • LI Qiaoli ,
  • HAN Hua ,
  • LI Qiuhui ,
  • ZENG Xi
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  • Department of Science, Wuhan University of Technology, Wuhan 430070, China

Received date: 2021-10-23

  Revised date: 2021-12-07

  Online published: 2023-04-19

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Abstract

The current similarity-based link prediction methods ignore the ability of the optimal path to transfer similarity between nodes. To solve this problem, a link prediction method based on the optimal path similarity transmission matrix is proposed. Firstly, the influence of the optimal path between nodes on the information transmission capacity is analyzed, then the tight centrality between nodes is defined; secondly, the number of optimal paths and centrality is used to construct the similarity transmission matrix, and the local information between nodes and global attributes are integrated to evaluate the similarity between nodes. Finally, the proposed method is compared with other similarity-based algorithms in six real networks. The results show that the proposed algorithm has more accurate prediction accuracy and is more stable.

Key words： complex networks; link prediction; optimal path; similarity transfer matrix; similarity measurement; centrality

Cite this article

LI Qiaoli , HAN Hua , LI Qiuhui , ZENG Xi , LI Qiaoli , HAN Hua , LI Qiuhui , ZENG Xi . Link Prediction Method Based on Optimal Path Similarity Transfer Matrix[J]. Complex Systems and Complexity Science, 2023 , 20(1) : 9 -17 . DOI: 10.13306/j.1672-3813.2023.01.002

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