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A scientometrics law about co-authors and their ranking: the co-author core

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Abstract

Rather than “measuring” a scientist impact through the number of citations which his/her published work can have generated, isn’t it more appropriate to consider his/her value through his/her scientific network performance illustrated by his/her co-author role, thus focussing on his/her joint publications, and their impact through citations? Whence, on one hand, this paper very briefly examines bibliometric laws, like the h-index and subsequent debate about co-authorship effects, but on the other hand, proposes a measure of collaborative work through a new index. Based on data about the publication output of a specific research group, a new bibliometric law is found. Let a co-author C have written J (joint) publications with one or several colleagues. Rank all the co-authors of that individual according to their number of joint publications, giving a rank r to each co-author, starting with r = 1 for the most prolific. It is empirically found that a very simple relationship holds between the number of joint publications J by coauthors and their rank of importance, i.e., J ∝ 1/r. Thereafter, in the same spirit as for the Hirsch core, one can define a “co-author core”, and introduce indices operating on an author. It is emphasized that the new index has a quite different (philosophical) perspective that the h-index. In the present case, one focusses on “relevant” persons rather than on “relevant” publications. Although the numerical discussion is based on one “main author” case, and two “control” cases, there is little doubt that the law can be verified in many other situations. Therefore, variants and generalizations could be later produced in order to quantify co-author roles, in a temporary or long lasting stable team(s), and lead to criteria about funding, career measurements or even induce career strategies.

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References

  • Abbasi, A., Altmann, J., & Hossain, L. (2011). Identifying the effects of co-authorship networks on the performance of scholars: A correlation and regression analysis of performance measures and social network analysis measures. Journal of Informetrics, 5, 594–607.

    Article  Google Scholar 

  • Alonso, S., Cabrerizo, F. J., Herrera-Viedma, E., & Herrera, F. (2009). h-index: A review focused in its variants, computation and standardization for different scientific fields. Journal of Informetrics, 3, 273–289.

    Article  Google Scholar 

  • Ausloos, M., Lambiotte, R., Scharnhorst, I. A., & Hellsten, I. (2008). Andrzej Pekalski networks of scientific interests with internal degrees of freedom through self-citation analysis. International Journal of Modern Physics C, 19, 371–384.

    Article  MATH  Google Scholar 

  • Beaver, D. de B. (2001). Reflections on scientific collaborations (and its study): Past, present and prospective. Scientometrics, 52, 365–377.

    Article  Google Scholar 

  • Beck, I. M. (1984). A method of measurement of scientific production. Science of Science, 4, 183–195.

    Google Scholar 

  • Börner, K., Dall’Asta, L., Ke, W., & Vespignani, A. (2005). Studying the emerging global brain: Analyzing and visualizing the impact of co-authorship teams. Complexity, 10, 57–67.

    Google Scholar 

  • Bornmann, L., Mutz, R., & Daniel, H. (2008). Are there better indices for evaluation purposes than the h-index? A comparison of nine different variants of the h-index using data from biomedicine. Journal of the American Society for Information Science and Technology, 59(5), 830–837.

    Article  Google Scholar 

  • Bruckner, E., Ebeling, W., & Scharnhorst, A. (1990). The application of evolution models in scientometrics. Scientometrics, 18, 21–41.

    Article  Google Scholar 

  • Buchanan, R. A. (2006). Accuracy of cited references: The role of citation databases. College and Research Libraries, 67, 292–303.

    Google Scholar 

  • Carbone, V. (2012). Fractional counting of authorship to quantify scientific research output. arxiv:1106.0114v1.

  • Chinchilla-Rodriguez, Z., Vargas-Quesada, B., Hassan-Montero, Y., Gonzàlez-Molina, A., & Moya-Anegón, F. (2010). New approach to the visualization of international scientific collaboration. Information Visualization, 9(4), 277–287.

    Article  Google Scholar 

  • Chung, K. H., & Cox, R. A. K. (1990). Patterns of productivity in the finance literature: A study of the bibliometric distributions. Journal of Finance, 45, 301–309.

    Article  Google Scholar 

  • de Solla Price, D. J. (1963). Little science, big science. New York: Columbia University Press.

    Google Scholar 

  • de Solla Price, D.J. (1978). Science since Babylon. New Haven: Yale University Press.

    Google Scholar 

  • de Solla Price, D. J., & Gürsey, S. (1975). Some statistical results for the numbers of authors in the states of the United States and the nations of the world. In Who is Publishing in Science, 1975 Annual. Philadelphia: Institute for Scientific Information.

  • Durieux, V., & Gevenois, P. A. (2010). Bibliometric indicators: Quality measurements of scientific publication. Radiology, 255(2), 342–351.

    Article  Google Scholar 

  • Egghe, L. (2008). Mathematical theory of the h- and g-index in case of fractional counting of authorship. Journal of the American Society for Information Science and Technology, 59, 1608–1616.

    Article  Google Scholar 

  • Egghe, L. (2010). The Hirsch index and related impact measures. Annual Review of Information Science and Technology, 44(1), 65–114.

    Article  Google Scholar 

  • Egghe, L., (2005). Power laws in the information production process. Lotkaian Informetrics.

  • Egghe, L., & Rousseau, R. (1990). Introduction to informetrics quantitative methods in library, documentation and information science. Amsterdam: Elsevier.

    Google Scholar 

  • Egghe, L., & Rousseau, R. (2012). The Hirsch index of a shifted Lotka function and its relation with the impact factor. Journal of the American Society for Information Science and Technology, 63(5), 1048–1053.

    Article  Google Scholar 

  • Galam, S. (2011). Tailor based allocations for multiple authorship: a fractional gh-index. Scientometrics, 89, 365–379.

    Article  Google Scholar 

  • Gilbert, G. N. (1978). Measuring the growth of science: A review of indicators of scientific growth. Scientometrics, 1, 9–34.

    Article  Google Scholar 

  • Glänzel, W., & Thijs, B. (2004). Does co-authorship inflate the share of self-citations?. Scientometrics, 61, 395–404.

    Article  Google Scholar 

  • Glänzel, W. (2003). Bibliometric as a research field: A course on theory and application of bibliometric indicators. Course Handouts. http://nsdl.niscair.res.in.

  • Hagen, N. T. (2009). Credit for coauthors. Science, 323, 583.

    Google Scholar 

  • Hellsten, I., Lambiotte, R., Scharnhorst, A., & Ausloos, M. (2006). A journey through the landscape of physics and beyond—the self-citation patterns of Werner Ebeling. In T. Poeschel, H. Malchow, & L. Schimansky-Geier (Eds.), Irreversible Prozesse und Selbstorganisation (pp. 375–384). Berlin: Logos Verlag.

    Google Scholar 

  • Hellsten, I., Lambiotte, R., Scharnhorst, A., & Ausloos, M. (2007a). Self-citations, co-authorships and keywords: A new method for detecting scientists field mobility? Scientometrics, 72, 469–486.

    Article  Google Scholar 

  • Hellsten, I., Lambiotte, R., Scharnhorst, A., Ausloos, M. (2007b). Self-citations networks as traces of scientific careers. In D. Torres-Salinas, & H. Moed (Ed.), Proceedings of the ISSI 2007, 11th International Conf. of the Intern. Society for Scientometrics and Informetrics, CSIC (Vol. 1, pp. 361–367), Madrid, Spain, June 25–27, 2007.

  • Hirsch, J. E. (2005). An index to quantify an individual’s scientific research output. Proceedings of the National Academy of Sciences USA, 102, 16569–16572.

    Article  Google Scholar 

  • Hirsch, J. E. (2010). An index to quantify an individual’s scientific research output that takes into account the effect of multiple coauthorship. Scientometrics, 85, 741–754.

    Article  Google Scholar 

  • Hollis, A. (2001). Co-authorship and the output of academic economists. Labour Economics, 8, 505–530.

    Article  Google Scholar 

  • Ioannidis, J. P. A. (2008). Measuring co-authorship and networking adjusted scientific impact. PLoS One 3.10.e2778.

  • Jin, B. (2006). h-index: An evaluation indicator proposed by scientist. Science Focus, 1(1), 8–9.

    Google Scholar 

  • Kealey, T. (2000). More is less. Economists and governments lag decades behind Derek Price’s thinking. Nature, 405, 279.

    Article  Google Scholar 

  • Kenna, R., & Berche, B. (2010). Critical mass and the dependency of research quality on group size. arxiv.org/pdf/1006.0928.

  • Kretschmer, H. (1985). Cooperation structure, group size and productivity in research groups. Scientometrics, 7, 39–53.

    Article  Google Scholar 

  • Kretschmer, H. (1987). The adaptation of the cooperation structure to the research process and scientific performances in research groups. Scientometrics, 12(5–6), 355–372.

    Google Scholar 

  • Kretschmer, H. (1994). Coauthorship networks of invisible colleges and institutional communities. Scientometrics, 30(1), 363–369.

    Article  Google Scholar 

  • Kretschmer, H. (1997). Patterns of behaviour in coauthorship networks of invisible colleges. Scientometrics, 40(3), 579–591.

    Article  Google Scholar 

  • Kretschmer, H. (1999). Collaboration, part II: Reflection of a proverb in scientific communities: Birds of a feather flock together. International Library Movement, 21(3), 113–134.

    Google Scholar 

  • Kretschmer, H. (2004). Author productivity and geodesic distance in co-authorship networks, and visibility on the Web. Scientometrics, 60, 409–420.

    Article  Google Scholar 

  • Kretschmer, H., & Rousseau, R. (2001). Author inflation leads to a breakdown of Lotka’s law. Journal of the American Society for Information Science and Technology, 52(8), 610–614.

    Article  Google Scholar 

  • Kretschmer, H., Kretschmer, U., Kretschmer, Th. (2007). Reflection of co-authorship networks in the Web: Web hyperlinks versus Web visibility rates. Scientometrics 70(2), 519–540

    Google Scholar 

  • Kwok, L. S. (2005). The White Bull effect: abusive coauthorship and publication parasitism. Journal of Medical Ethics, 31, 554–556.

    Article  Google Scholar 

  • Laherrère, J., & Sornette, D. (1998). Stretched exponential distributions in nature and economy: fat tails with characteristic scales. Eur. Phys. J. B, 2, 525–539.

    Article  Google Scholar 

  • Laudel, G. (2001). What do we measure by co-authorships? In M. Davis, & C. S. Wilson (Eds.), Proceedings of the 8th International Conference on Scientometrics and Informetrics (pp. 369–384). Sydney: Bibliometrics & Informetrics Research Group.

  • Lee, S., & Bozeman, B. (2005). The impact of research collaboration on scientific productivity. Social Studies of Science, 35(5), 673–702.

    Article  Google Scholar 

  • Li, W. (2002). Zipf’s law everywhere. Glottometrics, 5, 15–41.

    Google Scholar 

  • Li, W. (2003). http://linkage.rockefeller.edu/wli/zipf/.

  • Liao, C. H., & Yen, H. R. (2012). Quantifying the degree of research collaboration: A comparative study of collaborative measures. Journal of Informetrics 6, 27–33 (five measures that quantify the degree of research collaboration, including the collaborative index, the degree of collaboration, the collaborative coefficient, the revised collaborative coefficient, and degree centrality).

  • Long, J. S. (1992). Measures of sex differences in scientific productivity. Social Forces 71(1), 159–178.

    Google Scholar 

  • Lotka, A. J. (1926). The frequency distribution of scientific productivity. Journal of the Washington Academy of Sciences, 16, 317–323.

    Google Scholar 

  • Mali, F., Kronegger, L., Doreian, P., & Ferligoj, A. (2012). Chapter 6, Dynamic Scientific Co-Authorship Networks. In A. Scharnhorst, K. Börner, & P. van den Besselaar (Eds.), Models of science dynamics: Encounters between complexity theory and information sciences (pp. 195–232). Berlin: Springer.

  • McDonald, K. A. (1995). Too many co-authors?. Chronicle of Higher Education, 41, 35–36.

    Google Scholar 

  • Melin, G., & Persson, O. (1996). Studying research collaboration using co-authorships. Scientometrics, 36, 363–377.

    Article  Google Scholar 

  • Nascimento, M. A., Sander, J., & Pound, J. (2003). Analysis of SIGMOD’s co-authorship graph. ACM SIGMOD Record Homepage Archive, 32, 8–10.

    Article  Google Scholar 

  • Newman, M. E. J. (2004). Coauthorship networks and patterns of scientific collaboration. Proceedings of the National Academy of Sciences USA, 101, 5200–5205.

    Article  Google Scholar 

  • Pao, M. L. (1986). An empirical examination of Lotkas law. Journal of the American Society for Information Science and Technology, 37(1), 26–33.

    Google Scholar 

  • Persson, O., Glänzel, W., & Danell, R. (2004). Inflationary bibliometric values: The role of scientific collaboration and the need for relative indicators in evaluative studies. Scientometrics, 60, 421–432.

    Article  Google Scholar 

  • Ponds, R., Van Oort, F., & Frenken, K. (2007). The geographical and institutional proximity of research collaboration. Papers in Regional Science, 86(3), 423–443.

    Article  Google Scholar 

  • Potter, W. G. (1988). Of making many books there is no end: Bibliometrics and libraries. The Journal of Academic Librarianship, 14, 238a–238c.

    Google Scholar 

  • Sauer, R. D. (1988). Estimates of the returns to quality and coauthorship in economic academia. The Journal of Political Economy, 96, 855–866.

    Article  Google Scholar 

  • Schreiber, M. (2007). Self-citation corrections for the Hirsch index. Europhysics Letters, 78, 30002.

    Article  Google Scholar 

  • Schreiber, M. (2008a). To share the fame in a fair way, h m for multi-authored manuscripts. New Journal of Physics, 10(040201), 1–9.

    Google Scholar 

  • Schreiber, M. (2008b). A modification of the h-index: The h(m)-index accounts for multi-authored manuscripts. Journal of Informetrics, 2, 211–216.

    Article  Google Scholar 

  • Schreiber, M. (2010a). How to modify the g-index for multi-authored manuscripts. Journal of Informetrics, 4(1), 42–54.

    Article  Google Scholar 

  • Schreiber, M. (2010b). Twenty Hirsch index variants and other indicators giving more or less preference to highly cited papers. Annalen der Physik (Berlin) 522(8), 536–554.

    Google Scholar 

  • Schreiber, M., Malesios, C. C., & Psarakis, S. (2012). Exploratory factor analysis for the Hirsch index, 17 h-type variants, and some traditional bibliometric indicators. Journal of Informetrics, 6, 347–358.

    Article  Google Scholar 

  • Sekercioglu, C. H. (2008). Quantifying coauthor contributions. Science, 322, 371.

    Google Scholar 

  • Sekercioglu, C. H. (2009). Response from Cagan H. Sekercioglu to Hagen (2009). Science, 30, 583.

    Google Scholar 

  • Slone, R. M. (1996). Coauthors contributions to major papers published in the AjR: Frequency of undeserved coauthorship. American Journal of Roentgenology (AJR), 167, 571–579.

    Article  Google Scholar 

  • Sonnenwald, D. H. (2007). Scientific collaboration. Annual Review of Information Science and Technology 41, 643–681.

    Google Scholar 

  • Vanclay, J. K. (2007). On the robustness of the h-index. Journal of the American Society for Information Science and Technology, 58(10), 1547–1550.

    Article  Google Scholar 

  • van Raan, A. F. J. (1996). Advanced bibliometric methods as quantitative core of peer review based evaluation and foresight exercises. Scientometrics, 36(3), 397–420.

    Article  Google Scholar 

  • Vitanov, K., & Ausloos, M. (2012). Knowledge epidemics and population dynamics models for describing idea diffusion. In A. Scharnhorst, K. Börner, & P. van den Besselaar (Eds.), Models of science dynamics: Encounters between complexity theory and information sciences (Chap. 3, pp. 69–125). Berlin: Springer.

    Google Scholar 

  • Vučković-Dekić, L. (2003). Authorship–coauthorship. Archive of Oncology, 11(3), 211–212.

    Article  Google Scholar 

  • Waltman, L., Tijssen, R. J. W., & van Eck, N. J. (2011). Globalisation of science in kilometres. Journal of Informetrics, 5, 574–582.

    Article  Google Scholar 

  • Yablonsky, A. I. (1980). On fundamental regularities of the distribution of scientific productivity. Scientometrics, 2, 3–34.

    Article  Google Scholar 

  • Zhang, C. T. (2009a). A proposal for calculating weighted citations based on author rank. EMBO Reports 10, 416–417.

    Google Scholar 

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

    Google Scholar 

  • Zhang, R. (2009). An index to link scientific productivity with visibility. arxiv.org/pdf/0912.3573.

  • Zipf, G. K. (1949). Human behavior and the principle of least effort: An introduction to human ecology. Cambridge: Addison-Wesley.

    Google Scholar 

  • Zuccala, A. (2006). Modeling the invisible college. Journal of the American Society for Information Science and Technology, 57(2), 152–168.

    Article  Google Scholar 

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Acknowledgments

The author gratefully acknowledges stimulating and challenging discussions with many wonderful colleagues at several meetings of the COST Action MP-0801, ‘Physics of Competition and Conflict’. In particular, thanks to O. Yordanov for organising the May 2012 meeting “Evaluating Science: Modern Scientometric Methods”, in Sofia, and challenging the author to present new results. All colleagues mentioned in the text have frankly commented upon the manuscript and enhanced its content. Reviewer comments have, no doubt, much improved the present version of the ms.

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Ausloos, M. A scientometrics law about co-authors and their ranking: the co-author core. Scientometrics 95, 895–909 (2013). https://doi.org/10.1007/s11192-012-0936-x

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