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A model for the dynamics of face-to-face interactions in social groups

Published online by Cambridge University Press:  06 March 2020

Marion Hoffman Open the ORCID record for Marion Hoffman [Opens in a new window] ,
Per Block Open the ORCID record for Per Block [Opens in a new window] ,
Timon Elmer Open the ORCID record for Timon Elmer [Opens in a new window]  and
Christoph Stadtfeld Open the ORCID record for Christoph Stadtfeld [Opens in a new window]
Marion Hoffman*
Affiliation:
Chair of Social Networks, ETH Zürich, Zürich, Switzerland (e-mails: [email protected], [email protected])
Per Block
Affiliation:
Department of Sociology, University of Oxford, OxfordOX1 1JD, UK (e-mail: [email protected])
Timon Elmer
Affiliation:
Chair of Social Networks, ETH Zürich, Zürich, Switzerland (e-mails: [email protected], [email protected])
Christoph Stadtfeld
Affiliation:
Chair of Social Networks, ETH Zürich, Zürich, Switzerland (e-mails: [email protected], [email protected])
*
*Corresponding author. Email: [email protected]
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Abstract

Face-to-face interactions in social groups are a central aspect of human social lives. Although the composition of such groups has received ample attention in various fields—e.g., sociology, social psychology, management, and educational science—their micro-level dynamics are rarely analyzed empirically. In this article, we present a new statistical network model (DyNAM-i) that can represent the dynamics of conversation groups and interpersonal interaction in different social contexts. Taking an actor-oriented perspective, this model can be applied to test how individuals’ interaction patterns differ and how they choose and change their interaction groups. It moves beyond dyadic interaction mechanisms and translates central social network mechanisms—such as homophily, transitivity, and popularity—to the context of interactions in group settings. The utility and practical applicability of the new model are illustrated in two social network studies that investigate face-to-face interactions in a small party and an office setting.

Keywords

Hocine CherifiStanley WassermanNetwork modelssocial interactionsgroup interactionsstatistical modelssocial sensorsrelational eventsactor-oriented models
Type
Research Article
Information
Network Science , Volume 8 , Issue S1: Complex Networks 2018 , July 2020 , pp. S4 - S25
Copyright
© Cambridge University Press 2020

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References

Allen, J., James, A. D., & Gamlen, P. (2007). Formal versus informal knowledge networks in r&d: A case study using social network analysis. R&d Management, 37(3), 179–196.Google Scholar
Amati, V., Lomi, A., & Mascia, D. (2019). Some days are better than others: Examining time-specific variation in the structuring of interorganizational relations. Social Networks, 57, 18–33.CrossRefGoogle Scholar
Bales, R. F. (1950). Interaction process analysis; a method for the study of small groups. Addison-Wesley.Google Scholar
Block, P. (2018). Network evolution and social situations. Sociological Science, 5, 402–431.CrossRefGoogle Scholar
Brandenberger, L. (2019). Predicting network events to assess goodness of fit of relational event models. Political Analysis, 27(4), 556–571.CrossRefGoogle Scholar
Brandes, U., Lerner, J., & Snijders, T. A. (2009). Networks evolving step by step: Statistical analysis of dyadic event data. In 2009 international conference on advances in Social network analysis and mining (pp. 200–205). IEEE.CrossRefGoogle Scholar
Butts, C. T. (2008). A relational event framework for social action. Sociological Methodology, 38(1), 155–200.CrossRefGoogle Scholar
Carley, K. M., & Krackhardt, D. (1996). Cognitive inconsistencies and non-symmetric friendship. Social Networks, 18(1), 1–27.CrossRefGoogle Scholar
Cattuto, C., den Broeck, W., Barrat, A., Colizza, V., Pinton, J.-F., & Vespignani, A. (2010). Dynamics of person-to-person interactions from distributed RFID sensor networks. PloS One, 5(7), e11596.CrossRefGoogle ScholarPubMed
Cohen, J. (1968). Weighted kappa: Nominal scale agreement provision for scaled disagreement or partial credit. Psychological Bulletin, 70(4), 213.CrossRefGoogle ScholarPubMed
Collins, R. (2014). Interaction ritual chains, vol. 62, Princeton university press.Google Scholar
Deuflhard, P. (2004). Newton methods for nonlinear problems, volume 35 of springer series in computational mathematics.Google Scholar
Dong, W., Olguin-Olguin, D., Waber, B., Kim, T., & Pentland, P. (2012). Mapping organizational dynamics with body sensor networks. In 2012 Ninth International Conference on Wearable and Implantable Body Sensor Networks (pp. 130–135). IEEE.CrossRefGoogle Scholar
Elmer, T., Chaitanya, K., Purwar, P., & Stadtfeld, C. (2019). The validity of RFID badges measuring face-to-face interactions. Behavior Research Methods, 51(5), 2120–2138.CrossRefGoogle ScholarPubMed
Feld, S. L. (1982). Social structural determinants of similarity among associates. American Sociological Review, 47, 797–801.CrossRefGoogle Scholar
Fischer, C. S. (1982). To dwell among friends: Personal networks in town and city. Chicago: University of Chicago Press.Google Scholar
Goffman, A. (2019). Go to more parties? Social occasions as home to unexpected Turning points in life trajectories. Social Psychology Quarterly, 82(1), 51–74CrossRefGoogle Scholar
Goffman, E. (1967). Interaction ritual: Essays on face-to-face interaction. Oxford, England: Aldine.Google Scholar
Holland, P. W., & Leinhardt, S. (1977). A dynamic model for social networks. Journal of Mathematical Sociology, 5(1), 5–20.CrossRefGoogle Scholar
Hong, H., Luo, C., & Chan, M. C. (2016). Socialprobe: Understanding social interaction through passive wifi monitoring. In Proceedings of the 13th international conference on mobile and Ubiquitous systems: Computing, networking and services (pp. 94–103). ACM.Google Scholar
Landis, J. R., & Koch, G. G. (1977). The measurement of observer agreement for categorical data. Biometrics, 33, 159–174.CrossRefGoogle ScholarPubMed
Lazarsfeld, P. F., & Merton, R. K. (1954). Friendship as a social process: A substantive and methodological analysis. Freedom and Control in Modern Society, 18(1), 18–66.Google Scholar
Lazega, E., Lemercier, C., & Mounier, U. (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.CrossRefGoogle Scholar
Lerner, J., & Lomi, A. (2019). Reliability of relational event model estimates under sampling: How to fit a relational event model to 360 million dyadic events. arXiv preprint arXiv:1905.00630.Google Scholar
Lewin, K. (1947). Frontiers in group dynamics: Concept, method and reality in social science; social equilibria and social change. Human Relations, 1(1), 5–41.CrossRefGoogle Scholar
Lusher, D., Koskinen, J., & Robins, G. (Eds.) (2013). Exponential random graph models for social networks. New York: Cambridge University Press.Google Scholar
Marcum, C. S., & Butts, C. T. (2015). Constructing and modifying sequence statistics for relevent using informr in r. Journal of Statistical Software, 64(5), 1–36.CrossRefGoogle Scholar
McFadden, D. (1974). Conditional logit analysis of qualitative choice behavior. In Zarembka, P. (Ed.), Frontiers in econometrics, chapter 4 (pp. 105–142). New York: Academic Press Inc.Google 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.CrossRefGoogle Scholar
Merton, R. K. (1968). The Matthew effect in science. Science, 159(3810), 56–63.CrossRefGoogle Scholar
Moody, J. (2001). Race, school integration, and friendship segregation in america. American Journal of Sociology, 107(3), 679–716.CrossRefGoogle Scholar
Mulder, J., & Leenders, R. T. A. (2019). Modeling the evolution of interaction behavior in social networks: A dynamic relational event approach for real-time analysis. Chaos, Solitons & Fractals, 119, 73–85.CrossRefGoogle Scholar
Olguín, D. O., Waber, B. N., Kim, T., Mohan, A., Ara, K., & Pentland, A. (2008). Sensible organizations: Technology and methodology for automatically measuring organizational behavior. IEEE Transactions on Systems, Man, and Cybernetics, Part B (Cybernetics), 39(1), 43–55.CrossRefGoogle Scholar
Pattison, P., & Robins, G. (2002). 9. neighborhood-based models for social networks. Sociological Methodology, 32(1), 301–337.CrossRefGoogle Scholar
Pentland, A. S. (2008). Honest signals. How hey shape our world. Cambridge, Massachussetts: The MIT Press.CrossRefGoogle Scholar
Perry, P. O., & Wolfe, P. J. (2013). Point process modelling for directed interaction networks. Journal of the Royal Statistical Society: Series B (Statistical Methodology), 75(5), 821–849.CrossRefGoogle Scholar
Potter, G. E., Smieszek, T., & Sailer, K. (2015). Modeling workplace contact networks: The effects of organizational structure, architecture, and reporting errors on epidemic predictions. Network Science, 3(3), 298–325.CrossRefGoogle ScholarPubMed
Robins, G. (2015). Doing social network research. London: SAGE.Google Scholar
Sailer, K., & McCulloh, I. (2012). Social networks and spatial configuration—how office layouts drive social interaction. Social Networks, 34(1), 47–58.CrossRefGoogle Scholar
Sailer, K., Marmot, A., & Penn, A. (2012). Spatial configuration, organisational change and academic networks. Paper presented at the Conference on Applied Social Network Analysis, 4–7 September 2012, Zürich, Switzerland.Google Scholar
Sapiezynski, P., Stopczynski, A., Gatej, R., & Lehmann, S. (2015). Tracking human mobility using wifi signals. PloS One, 10(7), e0130824.CrossRefGoogle ScholarPubMed
Schecter, A., Pilny, A., Leung, A., Poole, M. S., & Contractor, N. (2018). Step by step: Capturing the dynamics of work team process through relational event sequences. Journal of Organizational Behavior, 39(9), 1163–1181.CrossRefGoogle Scholar
Schroeder, H., & Lovell, H. (2012). The role of non-nation-state actors and side events in the international climate negotiations. Climate Policy, 12(1), 23–37.CrossRefGoogle Scholar
Sekara, V., Stopczynski, A., & Lehmann, S. (2016). Fundamental structures of dynamic social networks. Proceedings of the National Academy of Sciences, 113(36), 9977–9982.CrossRefGoogle ScholarPubMed
Simmel, G., & Hughes, E. C. (1949). The sociology of sociability. American Journal of Sociology, 55(3), 254–261.CrossRefGoogle ScholarPubMed
Snijders, T. A. (1996). Stochastic actor-oriented models for network change. Journal of Mathematical Sociology, 21(1–2), 149–172.CrossRefGoogle Scholar
Snijders, T. A. (2017). Stochastic actor-oriented models for network dynamics. Annual Review of Statistics and its Application, 4, 343–363.CrossRefGoogle Scholar
Snijders, T. A. & Lomi, A. (2019). Beyond homophily: Incorporating actor variables in statistical network models. Network Science, 7(1), 1–19.CrossRefGoogle Scholar
Snijders, T. A., Van de Bunt, G. G., & Steglich, C. E. (2010). Introduction to stochastic actor-based models for network dynamics. Social Networks, 32(1), 44–60.CrossRefGoogle Scholar
Spagnola, M., & Fiese, B. H. (2007). Family routines and rituals: A context for development in the lives of young children. Infants & Young Children, 20(4), 284–299.CrossRefGoogle Scholar
Stadtfeld, C. (2012). Events in social networks. A stochastic actor-oriented framework for dynamic event processes in social networks. Karlsruhe: KIT Scientific Publishing.Google Scholar
Stadtfeld, C., & Block, P. (2017). Interactions, actors, and time: Dynamic network actor models for relational events. Sociological Science, 4(1), 318–352.CrossRefGoogle Scholar
Stadtfeld, C., & Geyer-Schulz, A. (2011). Analyzing event stream dynamics in two-mode networks: An exploratory analysis of private communication in a question and answer community. Social Networks, 33(4), 258–272.CrossRefGoogle Scholar
Stadtfeld, C., Hollway, J., & Block, P. (2017). Dynamic network actor models: Investigating coordination ties through time. Sociological Methodology, 47(1), 1–40.CrossRefGoogle Scholar
Tajfel, H., Turner, J. C. (1979). An integrative theory of intergroup conflict. In Austin, W.G. & Worchel, S. (Eds.), The Social Psychology of Intergroup Relations. Monterey: Brooks-Cole.Google Scholar
Tajfel, M. (1970). Experiments in intergroup discrimination. Scientific American, 223, 96–102.CrossRefGoogle Scholar
Verbrugge, L. M. (1977). The structure of adult friendship choices. Social Forces, 56(2), 576–597.CrossRefGoogle Scholar
Vu, D., Pattison, P., & Robins, G. (2015). Relational event models for social learning in moocs. Social Networks, 43, 121–135.CrossRefGoogle Scholar
Wang, W., Bai, X., Xia, F., Bekele, T. M., Su, X., & Tolba, A. (2017). From triadic closure to conference closure: The role of academic conferences in promoting scientific collaborations. Scientometrics, 113(1), 177–193.CrossRefGoogle Scholar
Wasserman, S. (1980). Analyzing social networks as stochastic processes. Journal of the American Statistical Association, 75(370), 280–294.CrossRefGoogle Scholar
Wineman, J., Hwang, Y., Kabo, F., Owen-Smith, J., & Davis, G. F. (2014). Spatial layout, social structure, and innovation in organizations. Environment and Planning B: Planning and Design, 41(6), 1100–1112.CrossRefGoogle Scholar
Wu, L., Waber, B. N., Aral, S., Brynjolfsson, E., & Pentland, A. (2008). Mining face-to-face interaction networks using sociometric badges: Predicting productivity in an it configuration task. Available at SSRN 1130251.CrossRefGoogle Scholar
Wynn, J. R. (2016). On the sociology of occasions. Sociological Theory, 34(3), 276–286.CrossRefGoogle Scholar