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Simulating Relational Event Histories: Why and How (2403.19329v3)

Published 28 Mar 2024 in cs.SI and stat.ME

Abstract: Many important social phenomena are characterized by repeated interactions among individuals over time such as email exchanges in an organization or face-to-face interactions in a classroom. To understand the underlying mechanisms of social interaction dynamics, statistical simulation techniques of longitudinal network data on a fine temporal granularity are crucially important. This paper makes two contributions to the field. First, we present statistical frameworks to simulate relational event networks under dyadic and actor-oriented relational event models which are implemented in a new R package 'remulate'. Second, we explain how the simulation framework can be used to address challenging problems in temporal social network analysis, such as model fit assessment, theory building, network intervention planning, making predictions, understanding the impact of network structures, to name a few. This is shown in three extensive case studies. In the first study, it is elaborated why simulation-based techniques are crucial for relational event model assessment which is illustrated for a network of criminal gangs. In the second study, it is shown how simulation techniques are important when building and extending theories about social phenomena which is illustrated via optimal distinctiveness theory. In the third study, we demonstrate how simulation techniques contribute to a better understanding of the longevity and the potential effect sizes of network interventions. Through these case studies and software, researchers will be able to better understand social interaction dynamics using relational event data from real-life networks.

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References (42)
  1. How Initial Prevalence Moderates Network-Based Smoking Change: Estimating Contextual Effects with Stochastic Actor Based Models. Journal of health and social behavior, 57(1):22–38.
  2. Network structure influence on simulated network interventions for behaviour change. Social Networks, 64:55–62.
  3. Fast unfolding of communities in large networks. Journal of Statistical Mechanics: Theory and Experiment, 2008(10):P10008.
  4. Box, G. E. P. (1976). Science and Statistics. Journal of the American Statistical Association, 71(356):791–799. Publisher: Taylor & Francis _eprint: https://www.tandfonline.com/doi/pdf/10.1080/01621459.1976.10480949.
  5. Brandenberger, L. (2019). Predicting Network Events to Assess Goodness of Fit of Relational Event Models. Political Analysis, 27(4):556–571.
  6. Brewer, M. B. (1991). The Social Self: On Being the Same and Different at the Same Time. Personality and Social Psychology Bulletin, 17(5):475–482.
  7. Butts, C. T. (2008). A Relational Event Framework for Social Action. Sociological Methodology, 38(1):155–200.
  8. A Relational Event Approach to Modeling Behavioral Dynamics. arXiv:1707.09902 [stat], pages 51–92.
  9. A Bootstrap Method for Goodness of Fit and Model Selection with a Single Observed Network. Scientific Reports, 9(1):16674.
  10. Developing Theory Through Simulation Methods. Academy of Management Review, 32(2):480–499.
  11. Stochastic blockmodeling of relational event dynamics. In Proceedings of the Sixteenth International Conference on Artificial Intelligence and Statistics, pages 238–246. PMLR. ISSN: 1938-7228.
  12. Growing Artificial Societies: Social Science from the Bottom Up. The MIT Press.
  13. E-mail communication patterns and job burnout. PLoS ONE, 13(3).
  14. Embedding time in positions: Temporal measures of centrality for social network analysis. Social Networks, 54:168–178.
  15. Simulation for the Social Scientist. Open University Press, Maidenhead, England ; New York, NY, 2nd ed edition.
  16. Probabilistic forecasts, calibration and sharpness. Journal of the Royal Statistical Society: Series B (Statistical Methodology), 69(2):243–268.
  17. Hamill, T. M. (2001). Interpretation of Rank Histograms for Verifying Ensemble Forecasts. Monthly Weather Review, 129(3):550–560.
  18. Discrete temporal models of social networks. Electronic Journal of Statistics, 4(none):585–605.
  19. Network Analysis with the Enron Email Corpus. Journal of Statistics Education, 23(2):null.
  20. A dynamic model for social networks. The Journal of Mathematical Sociology, 5(1):5–20.
  21. Goodness of Fit of Social Network Model. Journal of the American Statistical Association, 103(481):248–258.
  22. Kleinbaum, A. M. (2012). Organizational misfits and the origins of brokerage in intrafirm networks. Administrative Science Quarterly, 57(3):407–452.
  23. Introducing the Enron Corpus. In CEAS.
  24. A multilevel approach to theory and research in organizations: Contextual, temporal, and emergent processes. In Multilevel Theory, Research, and Methods in Organizations: Foundations, Extensions, and New Directions, pages 3–90. Jossey-Bass/Wiley, Hoboken, NJ, US.
  25. A Separable Model for Dynamic Networks. Journal of the Royal Statistical Society. Series B, Statistical Methodology, 76(1):29–46.
  26. Once upon a time: Understanding team processes as relational event networks. Organizational Psychology Review, 6(1):92–115.
  27. Optimal Distinctiveness Theory. In Advances in Experimental Social Psychology, volume 43, pages 63–113. Elsevier.
  28. Goodness of fit for stochastic actor-oriented models. Methodological Innovations, 12(3):2059799119884282.
  29. Modeling the evolution of interaction behavior in social networks: A dynamic relational event approach for real-time analysis. Chaos, Solitons & Fractals, 119:73–85.
  30. Newman, M. E. J. (2006). Modularity and community structure in networks. Proceedings of the National Academy of Sciences of the United States of America, 103(23):8577–8582.
  31. Graph Metrics for Temporal Networks. arXiv:1306.0493 [physics], pages 15–40.
  32. Point process modelling for directed interaction networks. Journal of the Royal Statistical Society: Series B (Statistical Methodology), 75(5):821–849.
  33. Short- and long-term stability in organizational networks: Temporal structures of project teams. Social Networks, 35(4):528–540.
  34. The Power, Accuracy, and Precision of the Relational Event Model. Organizational Research Methods, page 1094428120963830.
  35. Introduction to stochastic actor-based models for network dynamics. Social Networks, 32(1):44–60.
  36. Representing micro–macro linkages by actor-based dynamic network models. Sociological Methods & Research, 44(2):222–271.
  37. Stadtfeld, C. (2014). Events in Social Networks : A Stochastic Actor-Oriented Framework for Dynamic Event Processes in Social Networks. KIT Scientific Publishing.
  38. Interactions, Actors, and Time: Dynamic Network Actor Models for Relational Events. Sociological Science, 4.
  39. Valente, T. W. (2005). Network Models and Methods for Studying the Diffusion of Innovations. In Scott, J., Carrington, P. J., and Wasserman, S., editors, Models and Methods in Social Network Analysis, Structural Analysis in the Social Sciences, pages 98–116. Cambridge University Press, Cambridge.
  40. Valente, T. W. (2012). Network Interventions. Science, 337(6090):49–53.
  41. Valente, T. W. (2017). Putting the network in network interventions. Proceedings of the National Academy of Sciences, 114(36):9500–9501.
  42. Model Adequacy Checking/Goodness-of-fit Testing for Behavior in Joint Dynamic Network/Behavior Models, with an Extension to Two-mode Networks. Sociological Methods & Research, 51(4):1886–1919.

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