Identifying the Academic Rising Stars via Pairwise Citation Increment Ranking

  • Chuxu Zhang
  • Chuang Liu
  • Lu Yu
  • Zi-Ke Zhang
  • Tao Zhou
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, 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 



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).


  1. 1.
    Bethard, S., Jurafsky, D.: Who should i cite: learning literature search models from citation behavior. In: CIKM (2010)Google Scholar
  2. 2.
    Dong, Y., Johnson, R.A., Chawla, N.V.: Will this paper increase your h-index?: scientific impact prediction. In: WSDM (2015)Google Scholar
  3. 3.
    Li, L., Tong, H., Tang, J., Fan, W.: ipath: forecasting the pathway to impact. In: SDM (2016)Google Scholar
  4. 4.
    Martin, T., Ball, B., Karrer, B., Newman, M.E.J.: Coauthorship and citation patterns in the physical review. Phys. Rev. E 88(1), 012814 (2013)CrossRefGoogle Scholar
  5. 5.
    Page, L., Brin, S., Motwani, R., Winograd, T.: The PageRank citation ranking: bringing order to the web. Technical report 1999-66, Stanford InfoLab (1999)Google Scholar
  6. 6.
    Petersen, A.M., Fortunato, S., Pan, R.K., Kaski, K., Penner, O., Rungi, A., Riccaboni, M., Stanley, H.E., Pammolli, F.: Reputation and impact in academic careers. Proc. Natl. Acad. Sci. U.S.A. 111(43), 15316–15321 (2014)CrossRefGoogle Scholar
  7. 7.
    Rendle, S., Freudenthaler, C., Gantner, Z., Schmidt-Thieme, L.: BPR: Bayesian personalized ranking from implicit feedback. In: UAI (2009)Google Scholar
  8. 8.
    Shen, H.-W., Wang, D., Song, C., Barabási, A.-L.: Modeling and predicting popularity dynamics via reinforced poisson processes. In: AAAI (2014)Google Scholar
  9. 9.
    Tang, J., Zhang, J., Yao, L., Li, J., Zhang, L., Arnetminer, Z.: Extraction and mining of academic social networks. In: KDD (2008)Google Scholar
  10. 10.
    Wang, D., Song, C., Barabási, A.-L.: Quantifying long-term scientific impact. Science 342(6154), 127–132 (2013)CrossRefGoogle Scholar
  11. 11.
    Yan, R., Huang, C., Tang, J., Zhang, Y., Li, X.: To better stand on the shoulder of giants. In: JCDL (2012)Google Scholar
  12. 12.
    Yan, R., Tang, J., Liu, X., Shan, D., Li, X.: Citation count prediction: learning to estimate future citations for literature. In: CIKM (2011)Google Scholar
  13. 13.
    Zhang, C., Yu, L., Lu, J., Zhou, T., Zhang, Z.-K.: AdaWIRL: a novel bayesian ranking approach for personal big-hit paper prediction. In: Cui, B., Zhang, N., Xu, J., Lian, X., Liu, D. (eds.) WAIM 2016. LNCS, vol. 9659, pp. 342–355. Springer, Cham (2016). doi: 10.1007/978-3-319-39958-4_27 Google Scholar

Copyright information

© Springer International Publishing AG 2017

Authors and Affiliations

  • Chuxu Zhang
    • 1
    • 2
  • Chuang Liu
    • 1
  • Lu Yu
    • 3
  • Zi-Ke Zhang
    • 1
  • Tao Zhou
    • 4
  1. 1.Alibaba Research Center for Complexity SciencesHangzhou Normal UniversityHangzhouChina
  2. 2.Department of Computer Science and EngineeringUniversity of Notre DameNotre DameUSA
  3. 3.King Abdullah University of Science and TechnologyThuwalSaudi Arabia
  4. 4.Big Data Research CenterUniversity of Electronic Science and Technology of ChinaChengduChina

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