  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 \Delta t 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.
