Developing a novel algorithm for comparing cluster patterns in networks on journal articles during and after COVID-19: Bibliometric analysis.

Background: Cluster analysis is vital in bibliometrics for deciphering large sets of academic data. However, no prior research has employed a cluster-pattern algorithm to assess the similarities and differences between 2 clusters in networks. The study goals are 2-fold: to create a cluster-pattern comparison algorithm tailored for bibliometric analysis and to apply this algorithm in presenting clusters of countries, institutes, departments, authors (CIDA), and keywords on journal articles during and after COVID-19.

Identifying China's distinctive academic fields among the top 2% of influential scientists: A bibliometric analysis using Rasch KIDMAP.

Background: Leading scientists worldwide are recognized by their placement in the top 2% based on their career-spanning contributions, as categorized by the Science-Metrix classification. However, there has been little focus on the unique scientific fields and subfields that separate countries. Although the KIDMAP in the Rasch model has been utilized to depict student performance, its application in identifying distinctive academic areas remains unexplored.