Skip to main content
Log in

The fractal dimension of a citation curve: quantifying an individual’s scientific output using the geometry of the entire curve

  • Published:
Scientometrics Aims and scope Submit manuscript

Abstract

Assorted bibliometric indices have been proposed leading to ambiguity in choosing the appropriate metric for evaluation. On the other hand, attempts to fit universal distribution patterns to scientific output have not converged to unified conclusions. To this end, we introduce the concept of fractal dimension to further examine the citation curve of an author. The fractal dimension of the citation curve could provide insight in its shape and form, level of skewness and distance from uniformity as well as the existing publishing patterns, without a priori assumptions on the particular citation distribution. It is shown that the notion of fractal dimension is not correlated to other well-known bibliometric indices. Further, a thorough experimentation of the fractal dimension is presented by using a set of 30,000 computer scientists and more than 9 million publications with over 38 million citations. The distinguishing power of the fractal dimension is investigated when comparing the impact of scientists and when trying to identify award winning scientists in their respective fields.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7
Fig. 8
Fig. 9

Similar content being viewed by others

Notes

  1. http://www.scimagojr.com/.

  2. http://www.eigenfactor.org/.

  3. http://amturing.acm.org/byyear.cfm.

  4. http://www.sigmod.org/sigmod-awards/.

  5. http://www.sigcomm.org/awards/sigcomm-awards.

  6. http://awards.acm.org/fellow/year.cfm.

References

  • Ashkenazy, Y. (1999). The use of generalized information dimension in measuring fractal dimension of time series. Physica A: Statistical Mechanics and Its Applications, 271(3–4), 427–447.

    Article  Google Scholar 

  • Batista, P. D., Campiteli, M. G., & Kinouchi, O. (2006). Is it possible to compare researchers with different scientific interests? Scientometrics, 68(1), 179–189.

    Article  Google Scholar 

  • Bollen, J., van de Sompel, H., Hagberg, A., & Chute, R. (2009). A principal component analysis of 39 scientific impact measures. PLOS One, 4(6), e6022.

    Article  Google Scholar 

  • Brin, S., & Page, L. (1998). The anatomy of a large-scale hypertextual web search engine. Computer Networks and ISDN Systems, 30(1–7), 107–117.

    Article  Google Scholar 

  • Brzezinski, M. (2015). Power laws in citation distributions: Evidence from Scopus. Scientometrics, 103(1), 213–228.

    Article  Google Scholar 

  • Callaway, E. (2016). Beat it, impact factor! Publishing elite turns against controversial metric. Nature, 535(7611), 210–211.

    Article  Google Scholar 

  • Chakraborty, T., Kumar, S., Goyal, P., Ganguly, N., & Mukherjee, A. (2015). On the categorization of scientific citation profiles in computer science. Communications of the ACM, 58(9), 82–90.

    Article  Google Scholar 

  • Egghe, L. (2006). Theory and practice of the g-index. Scientometrics, 69(1), 131–152.

    Article  Google Scholar 

  • Egghe, L., & Rousseau, R. (2008). An h-index weighted by citation impact. Information Processing & Management, 44(2), 770–780.

    Article  Google Scholar 

  • Eom, Y. H., & Fortunato, S. (2011). Characterizing and modeling citation dynamics. PLoS One, 6(9), e24,926.

    Article  Google Scholar 

  • Falconer, K. J., & Lammering, B. (1998). Fractal properties of generalized Sierpiński triangles. Fractals, 6(1), 31–41.

    Article  MathSciNet  MATH  Google Scholar 

  • Faloutsos, C., & Kamel, I. (1997). Relaxing the uniformity and independence assumptions using the concept of fractal dimension. Journal of Computer and System Sciences, 55(2), 229–240.

    Article  MathSciNet  MATH  Google Scholar 

  • Feng, J., Lin, W. C., & Chen, C. T. (1996). Fractional box-counting approach to fractal dimension estimation. In Proceedings 13th International Conference on Pattern Recognition (ICPR) (vol. 2, pp. 854–858).

  • Gagolewski, M., & Grzegorzewski, P. (2009). A geometric approach to the construction of scientific impact indices. Scientometrics, 81(3), 617.

    Article  Google Scholar 

  • Garanina, O. S., & Romanovsky, M. Y. (2016). Citation distribution of individual scientist: approximations of stretch exponential distribution with power law tails. arxiv:1605.03741

  • Garfield, E. (1955). Citation indexes for science: A new dimension in documentation through association of ideas. Science, 122, 108–111.

    Article  Google Scholar 

  • Glänzel, W., Beck, R., Milzow, K., Slipersæter, S., Tóth, G., Kołodziejski, M., et al. (2016). Data collection and use in research funding and performing organisations. General outlines and first results of a project launched by Science Europe. Scientometrics, 106(2), 825–835.

    Article  Google Scholar 

  • Gouyet, J. F. (1996). Physics and fractal structures. Berlin: Springer.

    MATH  Google Scholar 

  • Gupta, H. M., Campanha, J. R., & Pesce, R. A. G. (2005). Power-law distributions for the citation index of scientific publications and scientists. Brazilian Journal of Physics, 35, 981–986.

    Article  Google Scholar 

  • Hirsch, J. E. (2005). An index to quantify an individual’s scientific research output. Proceedings of the National Academy of Sciences, 102(46), 16,569–16,572.

    Article  MATH  Google Scholar 

  • Ibez, A., Armaanzas, R., Bielza, C., & Larraaga, P. (2016). Genetic algorithms and Gaussian Bayesian networks to uncover the predictive core set of bibliometric indices. Journal of the Association for Information Science and Technology, 67(7), 1703–1721.

    Article  Google Scholar 

  • Jin, B., Liang, L., Rousseau, R., & Egghe, L. (2007). The R- and AR-indices: Complementing the h-index. Chinese Science Bulletin, 52(6), 855–863.

    Article  Google Scholar 

  • Komulainen, T. (2004). Self-similarity and power laws. In H. Hyötyniemi (Ed.), Complex systems-science on the Edge of Chaos (vol. 145, pp. 109–122). http://neocybernetics.com/report145/chapter10.pdf

  • Miller, C. W. (2006). Superiority of the h-index over the Impact Factor for physics. http://arxiv.org/pdf/physics/0608183.pdf

  • Nykl, M., Jezek, K., Fiala, D., & Dostál, M. (2014). Pagerank variants in the evaluation of citation networks. Journal of Informetrics, 8(3), 683–692.

    Article  Google Scholar 

  • Osborne, A. R., & Provenzale, A. (1989). Finite correlation dimension for stochastic systems with power-law spectra. Physica D: Nonlinear Phenomena, 35(3), 357–381.

    Article  MathSciNet  MATH  Google Scholar 

  • Peterson, G. J., Pressé, S., & Dill, K. (2010). Nonuniversal power law scaling in the probability distribution of scientific citations. Proceedings of the National Academy of Sciences, 107(37), 16,023–16,027.

    Article  Google Scholar 

  • Radicchi, F., & Castellano, C. (2012). A reverse engineering approach to the suppression of citation biases reveals universal properties of citation distributions. PLoS One, 7(3), 1–9.

    Article  Google Scholar 

  • Radicchi, F., Fortunato, S., & Castellano, C. (2008). Universality of citation distributions: Toward an objective measure of scientific impact. Proceedings of the National Academy of Sciences, 105(45), 17,268–17,272.

    Article  Google Scholar 

  • Riikonen, P., & Vihinen, M. (2008). National research contributions: A case study on Finnish biomedical research. Scientometrics, 77(2), 207–222.

    Article  Google Scholar 

  • Rubem, A. P. S., de Moura, A. L., & Soares de Mello, J. B. (2015). Comparative analysis of some individual bibliometric indices when applied to groups of researchers. Scientometrics, 102(1), 1019–1035.

    Article  Google Scholar 

  • Sidiropoulos, A., Katsaros, D., & Manolopoulos, Y. (2007). Generalized Hirsch h-index for disclosing latent facts in citation networks. Scientometrics, 72(2), 253–280.

    Article  Google Scholar 

  • Sidiropoulos, A., Katsaros, D., & Manolopoulos, Y. (2015). Identification of influential scientists vs. mass producers by the Perfectionism index. Scientometrics, 103(1), 1–31.

    Article  Google Scholar 

  • Sidiropoulos, A., Gogoglou, A., Katsaros, D., & Manolopoulos, Y. (2016). Gazing at the skyline for star scientists. Journal of Informetrics, 10(3), 789–813.

    Article  Google Scholar 

  • Silagadze, Z. K. (2010). Citation entropy and research impact estimation. Acta Physica Polonica B, 41(11), 2325–2333.

    Google Scholar 

  • Song, C., Havlin, S., & Makse, H. (2005). Self-similarity of complex networks. Nature, 433(7024), 392–395.

    Article  Google Scholar 

  • Stringer, M. J., Sales-Pardo, M., & Nunes, L. A. (2010). Statistical validation of a global model for the distribution of the ultimate number of citations accrued by papers published in a scientific journal. Journal of the American Society for Information Science and Technology, 61(7), 1377–1385.

    Article  Google Scholar 

  • Traina, C, Jr., Traina, A. J. M., Wu, L., & Faloutsos, C. (2010). Fast feature selection using fractal dimension. Journal on Information and Database Management, 1(1), 3–16.

    Google Scholar 

  • Wallace, M. L., Larivière, V., & Gingras, Y. (2009). Modeling a century of citation distributions. Journal of Informetrics, 3(4), 296–303.

    Article  Google Scholar 

  • Wildgaard, L. (2015). A comparison of 17 author-level bibliometric indicators for researchers in astronomy, environmental science, philosophy and public health in web of science and google scholar. Scientometrics, 104(3), 873–906.

    Article  Google Scholar 

  • Wildgaard, L., Schneider, J. W., & Larsen, B. (2014). A review of the characteristics of 108 author-level bibliometric indicators. Scientometrics, 101(1), 125–158.

    Article  Google Scholar 

  • Wohlin, C. (2009). A new index for the citation curve of researchers. Scientometrics, 81(2), 521–533.

    Article  Google Scholar 

  • Ye, F., & Rousseau, R. (2010). Probing the h-core: An investigation of the tail-core ratio for rank distributions. Scientometrics, 84(2), 431–439.

    Article  Google Scholar 

  • Zhang, C. T. (2009). The e-index, complementing the h-index for excess citations. PLoS One, 4(5), e5429.

    Article  Google Scholar 

  • Zhang, H., Hu, Y., Lan, X., Mahadevan, S., & Deng, Y. (2014). Fuzzy fractal dimension of complex networks. Applied Soft Computing, 25, 514–518.

    Article  Google Scholar 

Download references

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to Antonia Gogoglou.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Gogoglou, A., Sidiropoulos, A., Katsaros, D. et al. The fractal dimension of a citation curve: quantifying an individual’s scientific output using the geometry of the entire curve. Scientometrics 111, 1751–1774 (2017). https://doi.org/10.1007/s11192-017-2285-2

Download citation

  • Received:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s11192-017-2285-2

Keywords

Navigation