PePSI: Personalized Prediction of Scholars’ Impact in Heterogeneous Temporal Academic Networks

The prediction of scholars’ scientific impact plays a significant role in accelerating the advancement of science, such as providing basis for the noble prizes, predicting the future influential scholars or research trends, offering tenures for researchers, and selecting promising candidates for research funding. Therefore, the study on scientific impact is of great significance and has drawn increasing interests. However, most current literature on predicting the impact of scholars neglect several vital facts, which are the time evolvement of academic networks, the distinct dynamics of different scholars’ impact, and the mutual influence among different scholarly entities. Inspired by the above-mentioned facts, we propose the PePSI solution for personalized prediction of scholars’ scientific impact. Our method primarily classifies scholars into different types according to their citation dynamics. For different scholars, we apply modified random walk algorithms to predict their impact in heterogeneous temporal academic networks with different time functions to capture the time-varying feature of academic networks. Experimental results on real data set demonstrate the effectiveness of PePSI in predicting top scholars and the overall impact of scholars with a rather short-term academic information as compared with the state-of-the-art prediction methods.