Profiling team exploration strategies of collaborating authors from artificial intelligence in computer science

Abstract

To identify collaboration trends with coauthors, this paper elaborates a theoretical framework by introducing a measure to quantify exploration of the author in joining teams of coauthors with respect to the extreme exploration possibilities. Using the clustering coefficient, we gauge the team exploration from the author-centric vista evaluating configuration values of the ego networks. This value is normalized with respect to the maximum exploration possibilities for the author facilitating us to derive a measure, viz., the team exploration score for the team exploration strategy. We further derive a dynamical version of this measure. The average profiles of the exploration strategies are compared for the authors from the USA, England, and India publishing in a rapidly growing and collaboration-extensive field, viz. artificial intelligence in computer science, in the time window from 1990 to 2020. The bibliometric data are sourced from the Clarivate Web of Science. Configuration values are evaluated in the ascending year of publications in year-long time windows to compute the team exploration score for each author. Our analysis shows that the annually averaged profiles of authors corresponding to the three countries are almost constantly increasing toward high team exploration scores. Also, in the career-averaged profiles, authors publishing more than 20 papers have mostly adopted exploratory strategies.