Predicting Star Scientists in the Field of Artificial Intelligence: A Machine Learning Approach

• URL: http://arxiv.org/abs/2407.14559v1
• Date: Thu, 18 Jul 2024 16:50:18 GMT
• Title: Predicting Star Scientists in the Field of Artificial Intelligence: A Machine Learning Approach
• Authors: Koosha Shirouyeh, Andrea Schiffauerova, Ashkan Ebadi,
• Abstract summary: This study proposes a model to predict star scientists in the field of artificial intelligence using machine learning techniques. We find that rising stars follow different patterns compared to their non-rising stars counterparts in almost all the early-career features. Gender and ethnic diversity play important roles in scientific collaboration and that they can significantly impact an author's career development and success.
• Score: 1.8434042562191815
• License: http://creativecommons.org/licenses/by/4.0/
• 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. Using machine learning techniques, this study proposes a model to predict star scientists in the field of artificial intelligence while highlighting features related to their success. Our results confirm that rising stars follow different patterns compared to their non-rising stars counterparts in almost all the early-career features. We also found that certain features such as gender and ethnic diversity play important roles in scientific collaboration and that they can significantly impact an author's career development and success. The most important features in predicting star scientists in the field of artificial intelligence were the number of articles, group discipline diversity, and weighted degree centrality. The proposed approach offers valuable insights for researchers, practitioners, and funding agencies interested in identifying and supporting talented researchers.

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