SOCIAL NETWORK MODELS AS MEDIATORS BETWEEN THEORY AND EMPIRICS EXAMPLE OF STATISTICAL MODELS OF NETWORK DYNAMICS
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Abstract
In this paper we present the argument for application, investigation and interpretation of a specific class of social network models as mediators between the theoretical and empirical perspectives on various problems of sociological interest. Our argument starts from the epistemological position which claims that (mathematical) models in science can mediate between theoretical constructs and data due to their epistemological autonomy. We claim that statistical models of network dynamics satisfy the criteria for this type of autonomy on the basis of: the way they are constructed, their research function, their application as an instrument and the possibility of learning from the models. After we briefly describe this class if network models, we present the case for first two criteria of autonomy and after reviewing recent applications in various social sciences we explain how these models function as an instrument and how they enable the researcher to learn from the results and use that knowledge for improvement of future research. These attributes are consequence of two statistical and mathematical properties of these models: the stochastic nature of data fitting and the modelling procedure focused on the individual actor rather than the aggregate data. In the discussion of the paper we claim that statistical models of network dynamics can offer a solution of bridging a gap between theory and data in several fields of social sciences. By emphasizing the need for autonomous models, we explain how they can improve the fruitfulness of the mathematical in social sciences with regard to existing theoretical pluralism and pre-paradigmatic state of social sciences.
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Bentler, P. M. (1990). Comparative fit indexes in structural models. Psychological Bulletin, 107(2), 238-246.
Bilinović, A. i V. Sokolovska (2016). Homofilija u socijalnim mrežama. Zbornik Matice srpske za društvene nauke 2(155-156), 325-338.
Burk, W. J., M. Kerr and H. Stattin (2008). The co-evolution of early adolescent friendship networks, school involvement, and delinquent behaviors. Revue française de sociologie, 49(3), 499-522.
Coleman, J. S. and T. Fararo (Eds.) (1992.). Rational Choice Theory: Advocacy and Critique. New York: Sage Publications.
Doreian, P., V. Batagelj and A. Ferligoj (2005). Generalized Blockmodeling. Cambridge University Press.
Kline, R. B. (2010). Principles and practice of structural equation modeling. London: The Guilford Press.
Kreps, D. M. (1990). Game Theory and Economic Modelling. Oxford: Clarendon Press.
Kronegger, L., F. Mali, A. Ferligoj and P. Doreian (2012). Collaboration structures in Slovenian scientific communities. Scientometrics, 90(2), 631-647.
Lazega, E., C. Lemercier and U. Mounier (2006). A spinning top model of formal organization and informal behavior: Dynamics of advice networks among judges in a commercial court. European management review, 3(2), 113-122.
Mali, F., A. Ferligoj, and L. Kronegger (2010). Co-authorship trends and collaboration patterns in the Slovenian sociological community. Corvinus Journal of Sociology and Social Policy, 1(2), 29-50.
McElreath, R. and R. Boyd (2007). Mathematical models of social evolution: A guide for the perplexed. Chicago: University of Chicago Press.
Morgan, M. S. and M. Morrison, M. (Eds.) (1999). Models as mediators: Perspectives on natural and social science. Cambridge: Cambridge University Press.
Nadel, S. F. (1958). The Theory of Social Structure. The Free Press of Glencoe.
Newman, M. E. (2001a). Scientific collaboration networks. I. Network construction and fundamental results. Physical review E, 64(1), 016131.
Newman, M. E. (2001b). Scientific collaboration networks. II. Shortest paths, weighted networks, and centrality. Physical review E, 64(1), 016132.
Newman, M. E. (2004). Fast algorithm for detecting community structure in networks. Physical review E, 69(6), 066133.
Newman, M. (2009). Networks: An introduction. Oxford: Oxford University Press.
Pondy, L. R. (1967). Organizational conflict: Concepts and models. Administrative science quarterly, 296-320.
Price, D. J. (1965). Networks of scientific papers. Science, 149(3683), 510-515.
Sokolovska Valentina, Tomašević Aleksandar, Dinić Bojana i Isidora Jarić (2016). Evolution of students' friendship networks: Examining the influence of group size. Etnoantropološki problemi 11(4), 1135-1151.
Van de Bunt, G. G., M. A. Van Duijn and T. A. Snijders (1999). Friendship networks through time: An actor-oriented dynamic statistical network model. Computational & Mathematical Organization Theory, 5(2), 167-192.
Van de Bunt, G. G., R. P. Wittek and M.C. de Klepper (2005). The Evolution of IntraOrganizational Trust Networks The Case of a German Paper Factory: An Empirical Test of Six Trust Mechanisms. International sociology, 20(3), 339-369.
Van de Bunt, G. G. and P. Groenewegen (2007). An actor-oriented dynamic network approach the case of interorganizational network evolution. Organizational Research Methods, 10(3), 463- 482.
Van Duijn, M. A., E. P. Zeggelink, M. Huisman, F. N. Stokman, and F. W. Wasseur (2003). Evolution of sociology freshmen into a friendship network. Journal of Mathematical Sociology, 27(2-3), 153-191.
Wasserman, S. and K. Faust (1994). Social network analysis: Methods and applications. Cambridge University press.
Watts, D. J. and S. H. Strogatz (1998). Collective dynamics of'small-world networks. Nature, 393(6684), 440-442.
Weil, A. (1949/1969). On the algebraic study of certain types of marriage laws (Murngin system). Appendix in: C. Lévi-Strauss. Elementary Structures of Kinship. Beacon Press, 221-230.
White, D. R. and F. Harary (2001). The cohesiveness of blocks in social networks: Node connectivity and conditional density. Sociological Methodology, 31(1), 305-359.