Link Prediction Method for Directed Networks Based on Path Connection Strength

Autor: ZHAO Xue-lei, JI Xin-sheng, LIU Shu-xin, LI Ying-le, LI Hai-tao
Jazyk: čínština
Rok vydání: 2022
Předmět:
Zdroj: Jisuanji kexue, Vol 49, Iss 2, Pp 216-222 (2022)
Druh dokumentu: article
ISSN: 1002-137X
DOI: 10.11896/jsjkx.210100107
Popis: 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.
Databáze: Directory of Open Access Journals