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Identifying the Academic Rising Stars via Pairwise Citation Increment Ranking

Part of the Lecture Notes in Computer Science book series (LNISA,volume 10366)


Predicting the fast-rising young researchers (the 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 \(\varDelta t\) years. We explore a series of factors that can drive an author to be fast-rising and design a novel pairwise citation increment ranking (PCIR) method 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 PCIR. Specifically, it outperforms all given benchmark methods, with over 8% average improvement. Further analysis demonstrates that temporal features are the best indicators for rising stars prediction, while venue features are less relevant.


  • Scientific impact prediction
  • Bayesian personalized ranking
  • Data engineering

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This work was partially supported by Natural Science Foundation of China (Grant Nos. 61673151, 61503110 and 61433014), Zhejiang Provincial Natural Science Foundation of China (Grant Nos. LY14A050001 and LQ16F030006).

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Correspondence to Chuang Liu .

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Zhang, C., Liu, C., Yu, L., Zhang, ZK., Zhou, T. (2017). Identifying the Academic Rising Stars via Pairwise Citation Increment Ranking. In: Chen, L., Jensen, C., Shahabi, C., Yang, X., Lian, X. (eds) Web and Big Data. APWeb-WAIM 2017. Lecture Notes in Computer Science(), vol 10366. Springer, Cham.

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