Learning multi-resolution representations of research patterns in bibliographic networks

作者：

Highlights：

• This study proposes a novel model for learning multi-resolution representations of bibliographic entities.

• The proposed methods extract substructures of bibliographic networks and simplify the substructures according to multiple levels of detailedness.

• The simplified subgraphs balance the learning opportunities of high- and low-performance bibliographic entities by co-occurring with both types of entities.

• The proposed model discovered more consistent and multifaceted features of the bibliographic entities than the existing network embedding models.

摘要

•This study proposes a novel model for learning multi-resolution representations of bibliographic entities.•The proposed methods extract substructures of bibliographic networks and simplify the substructures according to multiple levels of detailedness.•The simplified subgraphs balance the learning opportunities of high- and low-performance bibliographic entities by co-occurring with both types of entities.•The proposed model discovered more consistent and multifaceted features of the bibliographic entities than the existing network embedding models.

论文关键词：Bibliographic network embedding,Skewed distribution,Multi-resolution representation learning,Level-wise simplification,Outstanding scholars

论文评审过程：Received 30 April 2020, Revised 5 December 2020, Accepted 7 December 2020, Available online 19 January 2021, Version of Record 19 January 2021.

论文官网地址：https://doi.org/10.1016/j.joi.2020.101126