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Universal trajectories of scientific success

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Abstract

Success of a scientific entity generally undergoes myriad vicissitudes, resulting in different patterns of success trajectories. Understanding and characterizing the rise and fall of scientific success is important not only from the perspective of designing new mathematical models but also to enhance the quality of various real-world systems such as scientific article search and recommendation systems. In this paper, we present a large-scale study of the subject by analyzing the success of two major scientific entities—papers and authors—in Computer Science and Physics. We quantify “success” in terms of citations and in the process discover six distinct success trajectories which are prevalent across multidisciplinary datasets. Our results reveal that these trajectories are not fully random, but are rather generated through a complex process. We further shed light on the behavior of these trajectories and unfold many interesting facets by asking fundamental questions—which trajectory is more successful, how significant and stable are these categories, what factors trigger the rise and fall of trajectories? A few of our findings sharply contradict the well-accepted beliefs on bibliographic research such as “Preferential Attachment”, “first-mover advantage”. We believe that this study will argue in favor of revising the existing metrics used for quantifying scientific success.

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Notes

  1. The annotators are experts on Bibliographic search.

References

  1. Barabasi A-L, Albert R (1999) Emergence of scaling in random networks. Science 286(5439):509–512

    Article  MathSciNet  MATH  Google Scholar 

  2. Barham P, Dragovic B, Fraser K, Hand S, Harris T, Ho A, Neugebauer R, Pratt I, Warfield A (2003) Xen and the art of virtualization. SIGOPS Oper Syst Rev 37(5):164–177. doi:10.1145/1165389.945462

    Article  Google Scholar 

  3. Beasley CJ (2005) Publish or perish. Lead Edge 24(9):872–872

    Article  Google Scholar 

  4. Bharathi DG (2013) Evaluation and ranking of researchers? Bh index. PLoS ONE 8(12):e82050. doi:10.1371/journal.pone.0082050

    Article  Google Scholar 

  5. Biscaro C, Giupponi C (2014) Co-authorship and bibliographic coupling network effects on citations. PLoS ONE 9(6):1–12

    Article  Google Scholar 

  6. Bollen J, Crandall DJ, Junk D, Ding Y, Börner K, Collective allocation of science funding: from funding agencies to scientific agency. arXiv:1304.1067

  7. Bornmann L, Daniel H (2006) Selecting scientific excellence through committee peer review–a citation analysis of publications previously published to approval or rejection of post-doctoral research fellowship applicants. Scientometrics 68(3):427–440

    Article  Google Scholar 

  8. Chakraborty T, Ganguly N, Mukherjee A (2014) Rising popularity of interdisciplinary research—an analysis of citation networks. In: Sixth international conference on communication systems and networks, COMSNETS 2014, Bangalore, 6–10 Jan 2014, pp 1–6. doi:10.1109/COMSNETS.2014.6734940

  9. Chakraborty T, Kumar S, Goyal P, Ganguly N, Mukherjee A (2014) Towards a stratified learning approach to predict future citation counts. In: JCDL, IEEE Computer Society, pp 351–360. http://dblp.uni-trier.de/db/conf/jcdl/jcdl2014.html#0002KGGM14

  10. Chakraborty T, Kumar S, Goyal P, Ganguly N, Mukherjee A (2015) On the categorization of scientific citation profiles in computer science. Commun ACM 58(9):82–90. doi:10.1145/2701412

    Article  Google Scholar 

  11. Chakraborty T, Kumar S, Reddy MD, Kumar S, Ganguly N, Mukherjee A (2013) Automatic classification and analysis of interdisciplinary fields in computer sciences. International conference on social computing (SocialCom). Alexandria, VA, pp 180–187

    Google Scholar 

  12. Chakraborty T, Sikdar S, Ganguly N, Mukherjee A (2014) Citation interactions among computer science fields: a quantitative route to the rise and fall of scientific research. Soc Netw Anal Min 4(1):187

    Article  Google Scholar 

  13. Chakraborty T, Sikdar S, Tammana V, Ganguly N, Mukherjee A (2013) Computer science fields as ground-truth communities: their impact, rise and fall. In: Advances in social networks analysis and mining 2013, ASONAM 13, Niagara, ON, Aug 25–29, 2013, pp 426–433. doi:10.1145/2492517.2492536

  14. Crespo JA, Ortuño-Ortín I, Ruiz-Castillo J (2012) The citation merit of scientific publications. PLoS ONE 7(11):1–9

    Google Scholar 

  15. de Solla Price D (1963) Little science, big science- and beyond (A Columbia paperback). Columbia University Press, New York

    Google Scholar 

  16. Della Sala S, Brooks J (2008) Multi-authors’ self-citation: a further impact factor bias? Cortex 44(9):1139–45

    Article  Google Scholar 

  17. Di Eugenio B, Glass M (2004) The kappa statistic: a second look. Comput Linguist 30(1):95–101. doi:10.1162/089120104773633402

    Article  MATH  Google Scholar 

  18. Egghe L (2006) Theory and practise of the g-index. Scientometrics 69(1):131–152

    Article  MathSciNet  Google Scholar 

  19. Fowler J, Aksnes D (2007) Does self-citation pay? Scientometrics 72(3):427–437

    Article  Google Scholar 

  20. Garfield E (1955) Citation indexes for science. A new dimension in documentation through association of ideas. Science 122: 1123–1127. http://www.garfield.library.upenn.edu/papers/science_v122v3159p108y1955.html

  21. Garfield E (1980) Premature discovery or delayed recognition–why? Curr Contents 21:5–10

    Google Scholar 

  22. Garfield E (1989) Delayed recognition in scientific discovery: citation frequency analysis aids the search for case history. Curr nt Contents 23:3–9

    Google Scholar 

  23. Garfield E (1999) Journal impact factor: a brief review. CMAJ 161(8):979–980

    Google Scholar 

  24. Garfield E (2006) The history and meaning of the journal impact factor. JAMA 295(1):90–93

    Article  Google Scholar 

  25. Gingras Y, Larivière V, Macaluso B, Robitaille J-P (2009) The effects of aging on researchers’ publication and citation patterns. PLoS ONE 3(12):1–8

    Google Scholar 

  26. Glänzel W, Schlemmer B, Thijs B (2003) Better late than never? On the chance to become highly cited only beyond the standard bibliometric time horizon. Scientometrics 58(3):571–586

    Article  Google Scholar 

  27. Guimera R, Uzzi B, Spiro J, Amaral L (2005) Team assembly mechanisms determine collaboration network structure and team performance. Science 308(5722):697–702

    Article  Google Scholar 

  28. Hajra KB, Sen P (2005) Aging in citation networks. Phys A 346(1–2):44–48

    Google Scholar 

  29. Hinton GE, Osindero S, Teh Y-W (2006) A fast learning algorithm for deep belief nets. Neural Comput 18(7):1527–1554

    Article  MathSciNet  MATH  Google Scholar 

  30. Hirsch JE (2005) An index to quantify an individual’s scientific research output. PNAS 102(46):16569–16572

    Article  MATH  Google Scholar 

  31. Ke Q, Ferrara E, Radicchi F, Flammini A (2015) Defining and identifying sleeping beauties in science. PNAS 112(24):7426–7431

    Article  Google Scholar 

  32. Kinney AL (2007) National scientific facilities and their science impact on nonbiomedical research. PNAS 104(46):17943–17947

    Article  Google Scholar 

  33. Kleinberg JM (1999) Authoritative sources in a hyperlinked environment. J ACM 46(5):604–632. doi:10.1145/324133.324140

    Article  MathSciNet  MATH  Google Scholar 

  34. Kulkarni AV, Aziz B, Shams I, Busse JW (2011) Author self-citation in the general medicine literature. PLoS ONE 6(6):1–5

    Article  Google Scholar 

  35. LeCun Y, Bottou L, Bengio Y, Haffner P (1998) Gradient-based learning applied to document recognition. Proc IEEE 86:2278–2324

    Article  Google Scholar 

  36. Li S, Yu G, Zhang X, Zhang WF (2014) Identifying princes of sleeping beauty—knowledge mapping in discovering princes. In: International conference on management science engineering (ICMSE), Helsinki, pp 912–918

  37. Liu NC, Cheng Y, Liu L (2005) Academic ranking of world universities using scientometrics–a comment to the “fatal attraction”. Scientometrics 64(1):101–109

    Article  Google Scholar 

  38. Meho LI (2007) The rise and rise of citation analysis. Phys World 1(20):32–36

    Article  Google Scholar 

  39. Newman M (2009) The first-mover advantage in scientific publication. Europhys Lett 86:68001

    Article  Google Scholar 

  40. Petersen AM, Stanley HE, Succi S (2011) Statistical regularities in the rank-citation profile of scientists. Sci Rep 1: doi:10.1038/srep00181

  41. Pradhan D, Paul PS, Maheswari U, Nandi S, Chakraborty T (2016) \(\text{C}^{3}\)-index: revisiting author’s performance measure. In: Proceedings of the 8th ACM conference on web science, WebSci 2016, Hannover, 22–25 May 2016, pp 318–319. doi:10.1145/2908131.2908185

  42. Pradhan D, Paul PS, Maheswari U, Nandi S, Chakraborty T (2017) \(\text{ C }^{3}\)-index: a pagerank based multi-faceted metric for authors’ performance measurement. Scientometrics 110(1):253–273. doi:10.1007/s11192-016-2168-y

    Article  Google Scholar 

  43. Radicchi F, Fortunato CS (2008) Universality of citation distributions: towards an objective measure of scientific impact. PNAS 105(45):17268–17272

    Article  Google Scholar 

  44. Rani S, Sikka G (2012) Article: Recent techniques of clustering of time series data: a survey. Int J Comput Appl 52(15):1–9 full text available

  45. Redner S (2005) Citation statistics from 110 years of physical review. Phys Today 58(6):49–54

    Article  Google Scholar 

  46. Scholkopf B, Smola AJ (2001) Learning with kernels: support vector machines, regularization, optimization, and beyond. MIT Press, Cambridge

    Google Scholar 

  47. Schreiber M (2007) Self-citation corrections for the Hirsch index. Europhys Lett 78:1–6

    Article  Google Scholar 

  48. Sekercioglu CH (2008) Quantifying coauthor contributions. Science 322(5900):371

    Article  Google Scholar 

  49. Small H (1973) Co-citation in the scientific literature: a new measure of the relationship between two documents. J Am Soc Inf Sci 24(4):265–269. doi:10.1002/asi.4630240406

    Article  Google Scholar 

  50. Sun X, Kaur J, Milojevic S, Flammini A, Menczer F (2013) Social dynamics of science. Sci Rep. doi:10.1038/srep01069

    Google Scholar 

  51. van Raan AFJ (2004) Sleeping beauties in science. Scientometrics 59(3):467–472

    Article  Google Scholar 

  52. Wallace ML, Larivière V, Gingras Y (2012) A small world of citations? The influence of collaboration networks on citation practices. PLoS ONE 7(3):1–10

    Article  Google Scholar 

  53. Wang D, Song C, Barabási A-L (2013) Quantifying long-term scientific impact. Science 342(6154):127–132

    Article  Google Scholar 

  54. Wendl MC (2007) H-index: however ranked, citations need context. Nature 449(7161):403

    Article  Google Scholar 

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Acknowledgements

We thank Saswata Pandit, Jason Filippou, Barbara Lewis and Fabio Pierazzi for their valuable suggestions.

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Correspondence to Tanmoy Chakraborty.

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A part of the research was done when the author was at University of Maryland, College Park, USA.

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Chakraborty, T., Nandi, S. Universal trajectories of scientific success. Knowl Inf Syst 54, 487–509 (2018). https://doi.org/10.1007/s10115-017-1080-y

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