Understanding hierarchical structural evolution in a scientific discipline: A case study of artificial intelligence

作者：

Highlights：

• We develop a hierarchical topic model HNMF, which can generate a topic tree layer by layer.

• We propose the definition of topic migration.

• A visualization method StructureFlow is designed to intuitively analyze structural changes of the topic-tree.

摘要

•We develop a hierarchical topic model HNMF, which can generate a topic tree layer by layer.•We propose the definition of topic migration.•A visualization method StructureFlow is designed to intuitively analyze structural changes of the topic-tree.

论文关键词：Topic evolution,Artificial intelligence,Hierarchical knowledge structure,Nonnegative matrix factorization,Evolutionary patterns,Visual analysis approach

论文评审过程：Received 15 August 2019, Revised 10 April 2020, Accepted 13 April 2020, Available online 29 May 2020, Version of Record 29 May 2020.

论文官网地址：https://doi.org/10.1016/j.joi.2020.101047