Rank2vec: Learning node embeddings with local structure and global ranking

作者：

Highlights：

• It considers both local structure and global structural roles.

• It is very efficient due to its low time complexity.

• It is effective on the task of multi-label classification.

• The representations preserve both the microscopic and macroscopic information.

摘要

•It considers both local structure and global structural roles.•It is very efficient due to its low time complexity.•It is effective on the task of multi-label classification.•The representations preserve both the microscopic and macroscopic information.

论文关键词：Network representation,Node embedding,Local structure,Global role

论文评审过程：Received 16 December 2018, Revised 22 June 2019, Accepted 22 June 2019, Available online 22 June 2019, Version of Record 28 June 2019.

论文官网地址：https://doi.org/10.1016/j.eswa.2019.06.045