An alternative topic model based on Common Interest Authors for topic evolution analysis
作者：
Highlights：
• Authors are more topically similar when they are from multigraph clusters
• Changes in the definition of topics lead to more human-readable topics
• Topics from only meta-data of the publication records show higher topical coherence
• Use of multiple bibliographic networks is beneficial to author-based topic modeling
• Author-based topic modeling allows effective merge/split topic evolution tracking
摘要
•Authors are more topically similar when they are from multigraph clusters•Changes in the definition of topics lead to more human-readable topics•Topics from only meta-data of the publication records show higher topical coherence•Use of multiple bibliographic networks is beneficial to author-based topic modeling•Author-based topic modeling allows effective merge/split topic evolution tracking
论文关键词：Topic modeling,Bibliographic network,Topic evolution,Scientometric
论文评审过程：Received 21 September 2019, Revised 26 March 2020, Accepted 27 March 2020, Available online 30 May 2020, Version of Record 30 May 2020.
论文官网地址：https://doi.org/10.1016/j.joi.2020.101040
