Predicting Long-Term Scientific Impact Based on Multi-Field Feature Extraction

Abstract

Nowadays, there have been many studies on evaluating the scientific impact of scholars. However, we still lack effective methods to predict long-term impact, especially 10 years in the future. Therefore, we propose a long-term scientific impact prediction model based on multi-field feature extraction. The workflow of our proposed model consists of feature engineering and model ensemble. In feature engineering, we extract attribute feature, time-series feature, and heterogeneous network feature based on three different fields. Moreover, when extracting heterogeneous network feature, we propose a scientific impact evaluation method based on heterogeneous academic network, which considers both the time of publication and author order factors. In the model ensemble, we adjust the basic model and noise model to the different training set to make full use of the information from the original dataset. The experiment results demonstrate that the proposed model can stably improve the accuracy of scholars' scientific impact prediction, and it also offers a prediction pattern for long-term prediction problem.

Keywords

• H-index prediction
• scientific impact
• machine learning
• heterogeneous network