Skip to main content
Published Online:https://doi.org/10.1027/1614-2241.2.1.48

We give a nontechnical introduction into recently developed methods for analyzing the coevolution of social networks and behavior(s) of the network actors. This coevolution is crucial for a variety of research topics that currently receive a lot of attention, such as the role of peer groups in adolescent development. A family of dynamic actor-driven models for the coevolution process is sketched, and it is shown how to use the SIENA software for estimating these models. We illustrate the method by analyzing the coevolution of friendship networks, taste in music, and alcohol consumption of teenagers.

References

  • Baerveldt C. , Snijders T. A. B. (1994). Influences on and from the segmentation of networks: Hypotheses and tests. Social Networks, 16, 213– 232 First citation in articleCrossrefGoogle Scholar

  • Boer P. , Huisman M. E. , Snijders T. A. B. , Zeggelink E. P. H. (2003). StOCNET: An Open Software System for the Advanced Statistical Analysis of Social Networks (Version 1.4) [Computer software]. Groningen, the Netherlands: ProGAMMA/ICS, University of Groningen. Retrieved November 05, 2005, from stat.gamma.rug.nl/stocnet/ First citation in articleGoogle Scholar

  • Bourdieu P. (1984). Distinction: A social critique of the judgement of taste . London: Routledge [|amp|] Kegan First citation in articleGoogle Scholar

  • Bryson B (1996). Anything but heavy metal: Symbolic exclusion and musical dislikes. American Sociological Review, 61, 884– 899 First citation in articleCrossrefGoogle Scholar

  • Bush H. , West P. , Michell L. (1997). The role of friendship groups in the uptake and maintenance of smoking amongst pre-adolescent and adolescent children: Distribution of frequencies (Working Paper No. 62 of the Medical Sociology Unit). Glasgow, Scotland: Medical Research Council First citation in articleGoogle Scholar

  • Carley K (1991). A theory of group stability. American Sociological Review, 56, 331– 354 First citation in articleCrossrefGoogle Scholar

  • Carrington P. , Scott J. , Wasserman S. (Eds.) (2005). Models and methods in social network analysis . New York: Cambridge University Press First citation in articleGoogle Scholar

  • Doreian P. (1990). Network autocorrelation models: Problems and prospects. In D. A. Griffith (Ed.) Spatial statistics: Past, present, future (pp 369-389). Ann Arbor: Michigan Document Services First citation in articleGoogle Scholar

  • Dorogovtsev S. N. , Goltsev A. V. , Mendes J. F. F. (2002). Ising model on networks with an arbitrary distribution of connections. Physical Review E, 66, 016104l 1– 5 First citation in articleCrossrefGoogle Scholar

  • Friedkin N. (1998). A structural theory of social influence . Cambridge: Cambridge University Press First citation in articleGoogle Scholar

  • Hogg M. A. , Abrams D. , Otten S. , Hinkle S. (2004). The social identity perspective. Intergroup relations, self-conception, and small groups. Small Group Research, 35, 246– 276 First citation in articleCrossrefGoogle Scholar

  • Holland P. W. , Leinhardt S. (1973). The structural implications of measurement error in sociometry. Journal of Mathematical Sociology, 3, 85– 111 First citation in articleCrossrefGoogle Scholar

  • Huisman M. E. , Snijders T. A. B. (2003). Statistical analysis of longitudinal network data with changing composition.. Sociological Methods [|amp|] Research, 32, 253– 287 First citation in articleCrossrefGoogle Scholar

  • Katz-Gerro T. (1999). Cultural consumption and social stratification: Leisure activities, musical tastes, and social location.. Sociological Perspectives, 42, 627– 646 First citation in articleCrossrefGoogle Scholar

  • Koskinen J. (2004). Essays on Bayesian inference for social networks . Unpublished doctoral dissertation, Stockholm University, Stockholm, Sweden First citation in articleGoogle Scholar

  • Latané B. , Nowak A. (1997). Self-organizing social systems: Necessary and sufficient conditions for the emergence of clustering, consolidation and continuing diversity.. Progress in Communication Science, 13, 43– 74 First citation in articleGoogle Scholar

  • Macy M. W. , Kitts J. , Flache A. , Benard S. (2003). Polarization in dynamic networks: A Hopfield model of emergent structure.. In R. Breiger, K. Carley, P. Pattison (Eds.) Dynamic social network modelling and analysis: Workshop summary and papers (pp. 162-173). Washington, DC: National Academies Press First citation in articleGoogle Scholar

  • Mark N. (1998). Beyond individual differences: Social differentiation from first principles. American Sociological Review, 63, 309– 330 First citation in articleCrossrefGoogle Scholar

  • McPherson J. M. , Smith-Lovin L. , Cook J. M. (2001). Birds of a feather: Homophily in social networks. Annual Review of Sociology, 27, 415– 444 First citation in articleCrossrefGoogle Scholar

  • Michell L. , Amos A. (1997). Girls, pecking order and smoking.. Social Science [|amp|] Medicine, 44, 1861– 1869 First citation in articleCrossrefGoogle Scholar

  • Michell L. , West P. (1996). Peer pressure to smoke: The meaning depends on the method.. Health Education Research Theory [|amp|] Practice, 11, 39– 49 First citation in articleCrossrefGoogle Scholar

  • Molenaar I. M. , Sijtsma K. , Boer P. (2000). MSP5 for Windows. A Program for Mokken Scale Analysis for Polytomous Items (Version 5.0) [Computer software]. Groningen, the Netherlands: iec ProGAMMA First citation in articleGoogle Scholar

  • Pearson M. , West P. (2003). Social network analysis and Markov processes in a longitudinal study of friendship groups and risk-taking. Connections, 25, 59– 76 First citation in articleGoogle Scholar

  • Roe K. (1985). Swedish youth and music: Listening patterns and motivations.. Communication Research, 12, 353– 362 First citation in articleCrossrefGoogle Scholar

  • Roe K. (1996). Music and identity among European youth: Music as communication. In P. Rutten (Ed.), Music in Europe: Part 2. Music, culture and society in Europe (pp. 85-97). Brussels, Belgium: European Music Office First citation in articleGoogle Scholar

  • Simmel G. (1904). Fashion. International Quarterly, 10, 130– 155 First citation in articleGoogle Scholar

  • Snijders T. A. B. (1996). Stochastic actor-oriented models for network change. Journal of Mathematical Sociology, 21, 149– 176 First citation in articleCrossrefGoogle Scholar

  • Snijders T. A. B. (2001). The statistical evaluation of social network dynamics. In M. E. Sobel, M. P. Becker (Eds.), Sociological methodology—2001 (pp. 361-395). Boston and London: Basil Blackwell First citation in articleGoogle Scholar

  • Snijders T. A. B. (2005). Models for longitudinal network data. In P. Carrington, J. Scott, S. Wasserman (Eds.), Models and methods in social network analysis (pp. 215-247). New York: Cambridge University Press First citation in articleGoogle Scholar

  • Snijders T. A. B. , Steglich C. , Schweinberger M. (in press) Modeling the co-evolution of networks and behavior. In K. van Montfort, H. Oud, A. Satorra (Eds.), Longitudinal models in the behavioral and related sciences. Mahwah, NJ: Lawrence Erlbaum First citation in articleGoogle Scholar

  • Snijders T. A. B. , Steglich C. , Schweinberger M. , Huisman M. (2005). SIENA version 2.1 [Computer software and manual]. Groningen, the Netherlands: ICS / Department of Sociology, University of Groningen. Retrieved (February 21, 2005). from http://stat.gamma.rug.nl/stocnet. First citation in articleGoogle Scholar

  • Snijders T. A. B. , van Duijn M. A. J. (1997). Simulation for statistical inference in dynamic network models.. In R. Conte, R. Hegselmann, P. Terna (Eds.), Simulating social phenomena (pp. 493-512). Berlin: Springer First citation in articleGoogle Scholar

  • Steglich C. , Snijders T. A. B. , Pearson M. (2004). Dynamic networks and behavior: Separating selection from influence. Manuscript submitted for publication First citation in articleGoogle Scholar

  • Suzuki T. , Best J. (2003). The emergence of trendsetters for fashions and fads: Kogaru in 1990s Japan. Sociological Quarterly, 44, 61– 79 First citation in articleCrossrefGoogle Scholar

  • van de Bunt G. G. , van Duijn M. A. J. , Snijders T. A. B. (1999). Friendship networks through time: An actor-oriented statistical network model. Computational [|amp|] Mathematical Organization Theory, 5, 167– 192 First citation in articleCrossrefGoogle Scholar

  • Veblen T. (1899). The theory of the leisure class . New York: Macmillan First citation in articleGoogle Scholar