Computer Science ›› 2022, Vol. 49 ›› Issue (2): 216-222.doi: 10.11896/jsjkx.210100107

• Database & Big Data & Data Science • Previous Articles     Next Articles

Link Prediction Method for Directed Networks Based on Path Connection Strength

ZHAO Xue-lei, JI Xin-sheng, LIU Shu-xin, LI Ying-le, LI Hai-tao   

  1. PLA Strategic Support Force Information Engineering University,Zhengzhou 450002,China
  • Received:2021-01-14 Revised:2021-04-07 Online:2022-02-15 Published:2022-02-23
  • About author:ZHAO Xue-lei,born in 1996,postgra-duate.His main research interests include complex network and link prediction.
    LIU Shu-xin,born in 1987,Ph.D.His main research interests include network evolution and network behavior analysis.
  • Supported by:
    National Natural Science Foundation of China(61803384).

Abstract: Link prediction aims to predict unknown links using available network topology information.Prediction methods based on paths perform well in undirected networks.However,paths of the same length have different node connection strength due to different type of links through the path in directed network.Traditional methods is difficult to distinguish the path heterogeneity.Given this,the difference in the strength of three types of directed links is first quantified in terms of the link weight matrix,then the connection strength of different heterogeneous classpaths between nodes is calculated and the effect of different paths under the same length path is distinguished.Finally,a directed network link prediction method based on the path connection strength is proposed by integrating the contribution of multi-order paths of different lengths.Validation of 9 real networks shows that accounting for differences in path connection strength effectively improves prediction performance under the AUC and Precision metrics.

Key words: Complex network, Connection strength, Directed paths, Link prediction

CLC Number: 

  • TP301.6
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