Predicting the Evolution of Scientific Output

Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 10448)

Abstract

Various efforts have been made to quantify scientific impact and identify the mechanisms that influence its future evolution. The first step is the identification of what constitutes scholarly impact and how it is measured. In this direction, various approaches focus on future citation count or h-index prediction at author or publication level, on fitting the distribution of citation accumulation or accurately identifying award winners, upcoming hot research topics or academic rising stars. A plethora of features have been contemplated as possible influential factors and assorted machine-learning methodologies have been adopted to ensure timely and accurate estimations. Here, we provide an overview of the field challenges, as well as a taxonomy of the existing approaches to identify the open issues that are yet to be addressed.

Keywords

Scientometrics Bibliographic data Predictive modeling 

References

  1. 1.
    Acuna, D.E., Allesina, S., Kording, K.P.: Future impact: predicting scientific success. Nature 489(7415), 201–202 (2012)CrossRefGoogle Scholar
  2. 2.
    Börner, K., Dall’Asta, L., Ke, W., Vespignani, A.: Studying the emerging global brain: analyzing and visualizing the impact of co-authorship teams. Complexity 10(4), 57–67 (2005)CrossRefGoogle Scholar
  3. 3.
    Bornmann, L., Leydesdorff, L., Wang, J.: How to improve the prediction based on citation impact percentiles for years shortly after the publication date? J. Inf. 8(1), 175–180 (2014)CrossRefGoogle Scholar
  4. 4.
    Bornmann, L., Mutz, R., Hug, S.E., Daniel, H.P.: A multilevel meta-analysis of studies reporting correlations between the \(h\) index and 37 different \(h\) index variants. J. Inf. 5(3), 346–359 (2011)CrossRefGoogle Scholar
  5. 5.
    Brizan, D.G., Gallagher, K., Jahangir, A., Brown, T.: Predicting citation patterns: defining and determining influence. Scientometrics 108(1), 183–200 (2016)CrossRefGoogle Scholar
  6. 6.
    Cao, X., Chen, Y., Liu, K.R.: A data analytic approach to quantifying scientific impact. J. Inf. 10(2), 471–484 (2016)CrossRefGoogle Scholar
  7. 7.
    Chakraborty, T., Kumar, S., Goyal, P., Ganguly, N., Mukherjee, A.: Towards a stratified learning approach to predict future citation counts. In: Proceedings 14th ACM/IEEE-CS Joint Conference on Digital Libraries (JCDL), pp. 351–360 (2014)Google Scholar
  8. 8.
    Chakraborty, T., Kumar, S., Goyal, P., Ganguly, N., Mukherjee, A.: On the categorization of scientific citation profiles in computer science. Commun. ACM 58(9), 82–90 (2015)CrossRefGoogle Scholar
  9. 9.
    Chaudhuri, S., Dayal, U., Narasayya, V.: An overview of business intelligence technology. Commun. ACM 54(8), 88–98 (2011)CrossRefGoogle Scholar
  10. 10.
    Davletov, F., Aydin, A.S., Cakmak, A.: High impact academic paper prediction using temporal and topological features. In: Proceedings 23rd ACM International Conference on Conference on Information and Knowledge Management (CIKM), pp. 491–498 (2014)Google Scholar
  11. 11.
    Dong, Y., Johnson, R.A., Chawla, N.V.: Can scientific impact be predicted? IEEE Trans. Big Data 2(1), 18–30 (2016)CrossRefGoogle Scholar
  12. 12.
    Garner, J., Porter, A.L., Newman, N.C.: Distance and velocity measures: using citations to determine breadth and speed of research impact. Scientometrics 100(3), 687–703 (2014)CrossRefGoogle Scholar
  13. 13.
    Hirsch, J.E.: An index to quantify an individual’s scientific research output. Proc. Natl. Acad. Sci. 102(46), 16569–16572 (2005)CrossRefMATHGoogle Scholar
  14. 14.
    Jones, B.F., Weinberg, B.A.: Age dynamics in scientific creativity. Proc. Natl. Acad. Sci. 108(47), 18910–18914 (2011)CrossRefGoogle Scholar
  15. 15.
    Ke, Q., Ferrara, E., Radicchi, F., Flammini, A.: Defining and identifying sleeping beauties in science. Proc. Natl. Acad. Sci. 112(24), 7426–7431 (2015)CrossRefGoogle Scholar
  16. 16.
    Klimek, P.S., Jovanovic, A., Egloff, R., Schneider, R.: Successful fish go with the flow: citation impact prediction based on centrality measures for term-document networks. Scientometrics 107(3), 1265–1282 (2016)CrossRefGoogle Scholar
  17. 17.
    Laurance, W.F., Useche, D.C., Laurance, S.G., Bradshaw, C.J.: Predicting publication success for biologists. Bioscience 63(10), 817 (2013)CrossRefGoogle Scholar
  18. 18.
    Li, J., Shi, D., Zhao, S.X., Ye, F.Y.: A study of the “heartbeat spectra” for “sleeping beauties”. J. Inf. 8(3), 493–502 (2014)CrossRefGoogle Scholar
  19. 19.
    Lü, L., Zhou, T.: Link prediction in complex networks: a survey. Phys. A Stat. Mech. Appl. 390(6), 1150–1170 (2011)CrossRefGoogle Scholar
  20. 20.
    Mazloumian, A.: Predicting scholars’ scientific impact. PLoS ONE 7(11), 1–5 (2012)CrossRefGoogle Scholar
  21. 21.
    McNamara, D., Wong, P., Christen, P., Ng, K.S.: Predicting high impact academic papers using citation network features. In: Li, J., Cao, L., Wang, C., Tan, K.C., Liu, B., Pei, J., Tseng, V.S. (eds.) PAKDD 2013. LNCS, vol. 7867, pp. 14–25. Springer, Heidelberg (2013). doi: 10.1007/978-3-642-40319-4_2 CrossRefGoogle Scholar
  22. 22.
    Merton, R.K.: The Matthew effect in science. Science 159(3810), 56–63 (1968)CrossRefGoogle Scholar
  23. 23.
    Nezhadbiglari, M., Gonçalves, M.A., Almeida, J.M.: Early prediction of scholar popularity. In: Proceedings 16th ACM/IEEE-CS on Joint Conference on Digital Libraries (JCDL), pp. 181–190 (2016)Google Scholar
  24. 24.
    Penner, O., Pan, R.K., Petersen, A.M., Fortunato, S.: The case for caution in predicting scientists’ future impact. Phys. Today 66(4), 8 (2013)CrossRefGoogle Scholar
  25. 25.
    Pobiedina, N., Ichise, R.: Citation count prediction as a link prediction problem. Appl. Intell. 44(2), 252–268 (2016)CrossRefGoogle Scholar
  26. 26.
    Pradhan, D., Paul, P.S., Maheswari, U., Nandi, S., Chakraborty, T.: C3-index: revisiting author’s performance measure. In: Proceedings 8th ACM Conference on Web Science (WebSci), pp. 318–319 (2016)Google Scholar
  27. 27.
    van Raan, A.F.J.: Sleeping beauties in science. Scientometrics 59(3), 467–472 (2004)CrossRefGoogle Scholar
  28. 28.
    Revesz, P.Z.: A method for predicting citations to the scientific publications of individual researchers. In: Proceedings 18th International Database Engineering and Applications Symposium (IDEAS), pp. 9–18 (2014)Google Scholar
  29. 29.
    Revesz, P.Z.: Data mining citation databases: a new index measure that predicts Nobel prizewinners. In: Proceedings 19th International Database Engineering and Applications Symposium (IDEAS), pp. 1–9 (2015)Google Scholar
  30. 30.
    Sayyadi, H., Getoor, L.: Futurerank: ranking scientific articles by predicting their future pagerank. In: Proceedings SIAM International Conference on Data Mining (SDM), pp. 533–544 (2009)Google Scholar
  31. 31.
    Schreiber, M.: How relevant is the predictive power of the \(h\)-index? A case study of the time-dependent Hirsch index. J. Inf. 7(2), 325–329 (2013)CrossRefGoogle Scholar
  32. 32.
    Sidiropoulos, A., Gogoglou, A., Katsaros, D., Manolopoulos, Y.: Gazing at the skyline for star scientists. J. Inf. 10(3), 789–813 (2016)CrossRefGoogle Scholar
  33. 33.
    Sidiropoulos, A., Manolopoulos, Y.: A citation-based system to assist prize awarding. ACM SIGMOD Rec. 34(4), 54–60 (2005)CrossRefGoogle Scholar
  34. 34.
    Sinatra, R., Wang, D., Deville, P., Song, C., Barabási, A.: Quantifying the evolution of individual scientific impact. Science 354(6312), aaf5239 (2016)CrossRefGoogle Scholar
  35. 35.
    de Solla Price, D.J.: Networks of scientific papers. Science 149(3683), 510–515 (1965)CrossRefGoogle Scholar
  36. 36.
    Vieira, E.S., Cabral, J.A., Gomes, J.A.: How good is a model based on bibliometric indicators in predicting the final decisions made by peers? J. Inf. 8(2), 390–405 (2014)CrossRefGoogle Scholar
  37. 37.
    Wang, S., Xie, S., Zhang, X., Li, Z., Yu, P.S., He, Y.: Coranking the future influence of multiobjects in bibliographic network through mutual reinforcement. ACM Trans. Intell. Syst. Technol. 7(4), 64:1–64:28 (2016)CrossRefGoogle Scholar
  38. 38.
    Way, S.F., Morgan, A.C., Clauset, A., Larremore, D.B.: The misleading narrative of the canonical faculty productivity trajectory. CoRR abs/1612.08228 (2016)Google Scholar
  39. 39.
    Wildgaard, L., Schneider, J.W., Larsen, B.: A review of the characteristics of \(108\) author-level bibliometric indicators. Scientometrics 101(1), 125–158 (2014)CrossRefGoogle Scholar
  40. 40.
    Xiao, S., Yan, J., Li, C., Jin, B., Wang, X., Yang, X., Chu, S.M., Zhu, H.: On modeling and predicting individual paper citation count over time. In: Proceedings 25th International Joint Conference on Artificial Intelligence (IJCAI), pp. 2676–2682 (2016)Google Scholar
  41. 41.
    Zhang, J., Ning, Z., Bai, X., Wang, W., Yu, S., Xia, F.: Who are the rising stars in academia? In: Proceedings 16th ACM/IEEE-CS on Joint Conference on Digital Libraries (JCDL), pp. 211–212 (2016)Google Scholar

Copyright information

© Springer International Publishing AG 2017

Authors and Affiliations

  1. 1.Department of InformaticsAristotle UniversityThessalonikiGreece

Personalised recommendations