Predicting scholar potential: a deep learning model on social capital features

Identifying scholars with potentials early in their careers is critical for informed evaluations, effective allocation of funding, and tenure decisions, which in turn propel advancements in science and technology. This paper investigates the impact of social capital features on the identification of such scholars. Utilizing a comprehensive dataset spanning from 1991 to 2020, extracted from the Microsoft Academic Knowledge Graph, we analyze the novelty values of 56,568 scholars’ future publications using disruption index. We identify potential scholars as those within the top 1% based on these values. Our approach involves extracting nine key features of structural, relational, and cognitive capital from the dynamic co-authorship networks of these scholars during their early career stages. The influence of these features on scholar identification is assessed through ablation experiments using an LSTM-based predictive model. Our findings underscore the critical importance of cognitive capital features in the identification process. Furthermore, the integration of structural and relational capital features markedly enhances the model’s predictive accuracy, achieving significant improvements in precision metrics. Notably, relational capital features demonstrate a greater influence than structural features in predicting scholar potentials. These results provide essential insights and practical implications for strategies aimed at recognizing and fostering outstanding academic talent.