Towards Award Prediction Based on Big Data Co-author Network

With the field of scholarly big data rising gradually, researchers have shown an increased interest in the study of academic careers and achievements. Although the massive academic data create a lot of opportunities to learn about scholars, it is still difficult to predict academic award winners. To solve this problem, we firstly propose a new indicator set generated by big data co-authorship network to effectively evaluate scholars. On this basis, we then build a Back Propagation Neural Network-based prediction model to predict recipients of the National Science Fund for Distinguished Young Scholars of China in the field of computer science. The experimental results of precision rate, recall rate and F1 prove that this model has good performance in award prediction. In addition to the methodological contribution, our research can not only help scholars get a better understanding of underlying mechanism contained in academic achievements, but also provide them with the support and guidance to manage their academic careers.