Identifying the Academic Rising Stars
arXiv:1606.05752
Abstract
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.
arXiv comment12 pages
References in corpus (8)
- An index to quantify an individual's scientific research output
- BPR: Bayesian Personalized Ranking from Implicit Feedback
- Who is the best connected scientist? A study of scientific coauthorship networks
- Does the h-index have predictive power?
- Reputation and Impact in Academic Careers
- Collective credit allocation in science
- Coauthorship and citation in scientific publishing
- Prediction of highly cited papers