2016/06/18 by Chuxu Zhang, Zhang, Chuxu, Chuang Liu +7 · 1 citation
Computer Science · #Advanced Graph Neural Networks #Digital Libraries (cs.DL) #Expert finding and Q&A systems #FOS: Computer and information sciences #Topic Modeling
paper · pdf · doi:10.48550/arxiv.1606.05752
openalex publication_date 2016/06/18 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28
Predicting the fast-rising young researchers (Academic Rising Stars) in the future provides useful guidance to the research community, e.g., offering competitive candidates to university for young faculty hiring as they are expected to have success academic careers. In this work, given a set of young researchers who have published the first first-author paper recently, we solve the problem of how to effectively predict the top k% researchers who achieve the highest citation increment in Δt years. We explore a series of factors that can drive an author to be fast-rising and design a novel impact increment ranking learning (IIRL) algorithm that leverages those factors to predict the academic rising stars. Experimental results on the large ArnetMiner dataset with over 1.7 million authors demonstrate the effectiveness of IIRL. Specifically, it outperforms all given benchmark methods, with over 8% average improvement. Further analysis demonstrates that the prediction models for different research topics follow the similar pattern. We also find that temporal features are the best indicators for rising stars prediction, while venue features are less relevant.