Classification by multi-semantic meta path and active weight learning in heterogeneous information networks
作者：
Highlights：
• The complex correlations in Heterogeneous Information Network are represented by meta path.
• Multi-semantic Meta path and jump path strengthen the associations between the nodes.
• The active weight learning method is proposed for multiple kinds of meta-path.
• The classification task in HINs reaches higher accuracy even with the small labeled data size.
• The performance of our approach achieves significant improvement than other methods.
摘要
•The complex correlations in Heterogeneous Information Network are represented by meta path.•Multi-semantic Meta path and jump path strengthen the associations between the nodes.•The active weight learning method is proposed for multiple kinds of meta-path.•The classification task in HINs reaches higher accuracy even with the small labeled data size.•The performance of our approach achieves significant improvement than other methods.
论文关键词：Classification,Meta-path,Heterogeneous information network,Active weight learning,Similarity matrix
论文评审过程：Received 2 July 2018, Revised 25 December 2018, Accepted 15 January 2019, Available online 15 January 2019, Version of Record 21 January 2019.
论文官网地址：https://doi.org/10.1016/j.eswa.2019.01.044
