Abstract

Star scientists are highly influential researchers who have made significant contributions to their field, gained widespread recognition, and often attracted substantial research funding. They are critical for the advancement of science and innovation, and they have a significant influence on the transfer of knowledge and technology to industry. Identifying potential star scientists before their performance becomes outstanding is important for recruitment, collaboration, networking, or research funding decisions. The objectives of this study are to develop a prediction method for star scientists in the artificial intelligence scientific ecosystem and to investigate the features related to their success. Bibliographic data was extracted from Scopus and data mining techniques were employed to gain insights into the authors’ discipline, gender, and ethnicity. The h-index was used as a proxy for research performance, and a dynamic profile of authors was established. Rising stars were found to have different patterns compared to their non-rising stars counterparts in almost all the early-career features. Social network analysis showed that certain features such as gender and ethnic diversity play important role in scientific collaboration and that they can significantly impact an author's career development and success. The prediction of rising stars was based on the author's early-career characteristics such as quantity and quality of research output, metrics obtained from social network analysis, and various diversity measures. Several classifiers in machine learning were trained, tested, implemented, and compared in the prediction task. It was shown that the Random Forest outperformed other classifiers and that the most important combination of features in predicting star scientists in the artificial intelligence field is the number of articles, group discipline diversity, and weighted degree centrality. Our findings highlight the importance of considering the authors' characteristics from different categories of features in the early stages of their careers to identify rising stars. This study offers valuable insights for researchers, practitioners, and funding agencies interested in identifying and supporting talented researchers.