Abstract
We combine two seemingly distinct perspectives regarding the modeling of network dynamics. One perspective is found in the work of physicists and mathematicians who formally introduced the small world model and the mechanism of preferential attachment. The other perspective is sociological and focuses on the process of cumulative advantage and considers the agency of individual actors in a network. We test hypotheses, based on work drawn from these perspectives, regarding the structure and dynamics of scientific collaboration networks. The data we use are for four scientific disciplines in the Slovene system of science. The results deal with the overall topology of these networks and specific processes that generate them. The two perspectives can be joined to mutual benefit. Within this combined approach, the presence of small-world structures was confirmed. However preferential attachment is far more complex than advocates of a single autonomous mechanism claim.
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Notes
In terms of substance, a rigid distinction between qualitative and quantitative approaches contributes little and focuses attention on a pointless division. Given that both approaches have merits and drawbacks, it seems more constructive to combine them to take advantage of their strengths.
The idea of cumulative advantages comes from the passage in Matthew’s Gospel: “For unto every one that hath shall be given, and he shall have abundance: but from him that hath not shall be taken away even that which he hath.” Hence the term ”the Matthew effect”. The first systematic representation of cumulative advantage in science was provided by Merton (1973). Following him, a research stream invoked the idea of cumulative advantage as a central explanatory principle for the social stratification of science. Merton’s studies were concerned with both organizational and functional aspects of science as an institution capable of self-regulation through scientists adopting a common set of norms about scientific conduct.
According to various social network analysts, the small-world model was inspired by the work of de Sola Pool and Kochen (1978) who partially formalized the much more famous application of Travers and Milgram (1969). It expresses the simple idea that any two individuals, selected randomly from almost anywhere on the planet, are ‘connected’ via a path of no more than a small number of intermediate acquaintances. The (limited) empirical evidence suggested that this small number is about 6. This notion became a popular idea in the Broadway play named Six Degrees of Separation. The first practical evidence for the existence of a small-world phenomenon was first provided by the psychologist Milgram (Berg 2004, p. 46). Milgram’s experimental result was regarded as a good starting point for analyzing the underlying structure of scientific co-authorship.
We included the following variables: the number of coauthors within the national border of the discipline and the number of coauthors coming from outside of the discipline.
In Slovenia, the organization of research group around the unique research topics is (artificially) encouraged by the use of some R&D policy instruments. One very important instrument of governmental R&D policy are research programs at public universities and institutes because they are financed by the Slovene Research Agency. These research programs cover research topics that are financed for very long periods (up to seven or more years). Such long-term financial stability of research programs reduces the flexibility of research topics. One consequence takes the form of rigid and closed research groups.
This does not imply that they are actually in a library, although most of them are. Researchers are required to document scientific production to a senior scientific librarian who verifies these documents and includes them in a listing of scientific documents.
In a ‘general typology’ of science in Slovenia, physics, mathematics and sociology are root (first level) disciplines while biotechnology belongs to biotechnological sciences and is therefore second-level category.
Newman (2001) found that the physical sciences have much higher clustering coefficients than biomedicine. He concluded that one reason for this is the ‘top down’ organization of laboratories in classical physics. In contrast, within biomedicine it is less common for two scientists to collaborate if they have another collaborator in common.
We exclude all scientists with degree zero for these plots.
References
Abramo, G., D’Angela, C.A., & Solazzi, M. (2011). The relationship between scientist’s research performance and the degree of internationalization of their research. Scientometrics, 86(3), 629–643.
Barabási, A. L., & Albert, R. (1999). Emergence of scaling in random networks. Science, 286, 509–512.
Barabási, A. L., Jeong, H., Neda, Z., Ravasz, E., Schubert, A., & Vicsek, T. (2002). Evolution of the social network of scientific collaborations. Physica A: Statistical Mechanics and its Applications, 311(3–4), 590–614.
Berg, C. (2004). Vernetzung als Syndrom. Risiken und Chancen von Vrenetzungsprozessen fuer eine nachhaltige Entwicklung. Frankfurt: Campus Verlag.
Börner, K., Dall’Asta, L., Ke, W., & Vespignani, A. (2005). Studying the emerging global brain: Analysing and visualizing the impact of co-autorship teams. Complexity, 10(4), 57–67.
Garfield, E. (1979). Is citation analysis a legitimate evaluation tool? Scientometrics, 1(4), 359–375.
Glänzel, W., & de Lange, C. (2002). A distributional approach to multinationality measures of international scientific collaboration. Scientometrics, 54(1), 75–89. doi:10.1023/A:1015684505035.
Hara, N., Solomon, P., Kim, S. L., & Sonnenwald, D. H. (2003). An emerging view of scientific collaboration: Scientists’ perspectives on collaboration and factors that impact collaboration. Journal of the American Society for Information Science and Technology, 54(10), 952–965. doi:10.1002/asi.10291.
Hargens, L. L. (1975). Patterns of scientific research. Washington, DC: American Sociological Association.
Kronegger, L., Ferligoj, A., & Doreian, P. (2011). On the dynamics of national scientific systems. Quality & Quantity, 45(5), 989–1015. doi:10.1007/s11135-011-9484-3.
Kuhn, T. S. (1996). The structure of scientific revolutions, 3rd edn. Chicago: University Of Chicago Press.
Merton, R. K. (1968). The Matthew effect in science. Science, 159, 56–63.
Merton, R. K. (1973). Sociology of science. Chicago: Chicago University Press.
Moody, J. (2004). The structure of a social science collaboration network: Disciplinary cohesion from 1963 to 1999. American Sociological Review, 69(2), 213–238.
Newman, M. E. J. (2000). Small worlds: The structure of social networks. Santa Fe: Santa Fe Institute.
Newman, M. E. J. (2001). The structure of scientific collaboration networks. Proceedings of the National Academy of Sciences of the United States of America, 98(2), 404–409. doi:10.1073/pnas.021544898.
Newman, M. E. J. (2004). Co-authorship networks and patterns of scientific collaboration. Proceedings of the National Academy of Sciences of the United States of America, 101(Suppl 1), 5200–5205.
Perc, M. (2010). Growth and structure of Slovenia’s scientific collaboration network. Journal of Informetrics, 4, 475–482. doi:10.1016/j.joi.2010.04.003.
Price, D. S. (1963). Little science, big science and beyond. New York: Columbia University Press.
Price, D. S. (1965). Networks of scientific papers. Science, 149, 510–515.
Price, D. S. (1976). A general theory of bibliometric and other cumulative advantage processes. Journal of the American Society for Information Science, 27(5), 292–306. doi:10.1002/asi.4630270505.
Robins, G. L., Woolcock, J., & Pattison, P. (2005). Small and other worlds: Global network structures from local processes. American Journal of Sociology, 110, 894–936.
Rodriguez, M. A., & Pepe, A. (2008). On the relationship between the structural and socioacademic communities of a co-authorship network. Journal of Informetrics, 2(3), 195–201. doi:10.1016/j.joi.2008.04.002.
Said, Y. H., Wegman, E. J., Sharabati, W. K., & Rigsby, J. (2008). Social networks of author–coauthor relationships. Computational Statistics & Data Analysis, 52(4), 2177–2184. doi:10.1016/j.csda.2007.07.021.
Snijders, T. A., Steglich, C., Schweinberger, M., & Huisman, K. (2008). Manual for SIENA Version 3.2. ICS. Groningen, Oxford: University of Groningen, Department of Statistics, University of Oxford.
Snijders, T. A., van de Bunt, G. G., & Steglich, C. (2010). Introduction to stochastic actor-based models for network dynamics. Social Networks, 32(1), 44–60. doi:10.1016/j.socnet.2009.02.004.
de Sola Pool, I., & Kochen, M. (1978). Contacts and influence. Social Networks, 1(1), 5–51. doi:10.1016/0378-8733(78)90011-4.
Travers, J., & Milgram, S. (1969). An experimental study of the small world problem. Sociometry, 32(4), 425–443. doi:10.2307/2786545.
Watts, D. J., & Strogatz, S. H. (1998). Collective dynamics of 'small-world' networks. Nature, 393(6684), 440–442. doi:10.1038/30918.
Ziman, J. (1994). Prometheus bound. Science in dynamic steady state. Cambridge: Cambridge University Press.
Acknowledgments
We are very grateful to the Institute of Information Science (IZUM), for the preparation of the datasets, to Tom Snijders and to the anonymous referees for their constructive comments.
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Kronegger, L., Mali, F., Ferligoj, A. et al. Collaboration structures in Slovenian scientific communities. Scientometrics 90, 631–647 (2012). https://doi.org/10.1007/s11192-011-0493-8
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DOI: https://doi.org/10.1007/s11192-011-0493-8