DAC: Descendant-aware clustering algorithm for network-based topic emergence prediction
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 evolution,Topic prediction,Clustering,Topic emergence prediction,Scientometrics
论文评审过程：Received 18 February 2022, Revised 13 July 2022, Accepted 29 July 2022, Available online 12 August 2022, Version of Record 12 August 2022.
论文官网地址：https://doi.org/10.1016/j.joi.2022.101320
