Hostname: page-component-cb9f654ff-d5ftd Total loading time: 0 Render date: 2025-08-07T13:28:21.526Z Has data issue: false hasContentIssue false
  • English
  • Français

A theoretical and empirical comparison of the temporal exponential random graph model and the stochastic actor-oriented model

Published online by Cambridge University Press:  25 April 2019

Philip Leifeld  and
Skyler J. Cranmer
Philip Leifeld*
Affiliation:
University of Essex, Department of Government, Wivenhoe Park, Colchester CO4 3SQ, UK (e-mail: philip@philipleifeld.com)
Skyler J. Cranmer
Affiliation:
The Ohio State University, Department of Political Science, 2032 Derby Hall, 154 North Oval Mall, Columbus, OH 43210, USA (e-mail: cranmer.12@osu.edu)
*
*Corresponding author. Email: philip@philipleifeld.com
Get access Rights & Permissions [Opens in a new window]

Abstract

The temporal exponential random graph model (TERGM) and the stochastic actor-oriented model (SAOM, e.g., SIENA) are popular models for longitudinal network analysis. We compare these models theoretically, via simulation, and through a real-data example in order to assess their relative strengths and weaknesses. Though we do not aim to make a general claim about either being superior to the other across all specifications, we highlight several theoretical differences the analyst might consider and find that with some specifications, the two models behave very similarly, while each model out-predicts the other one the more the specific assumptions of the respective model are met.

Keywords

p*ERGMTERGMSAOMtemporal exponential random graph modelstochastic actor-oriented modellongitudinal networksinferential network analysisdynamic networksstatistical modeling

Information

Type
Original Article
Information
Network Science , Volume 7 , Issue 1 , March 2019 , pp. 20 - 51
Copyright
© Cambridge University Press 2019 

Access options

Get access to the full version of this content by using one of the access options below. (Log in options will check for institutional or personal access. Content may require purchase if you do not have access.)

Article purchase

Temporarily unavailable

References

Amati, V. (2011). New statistics for the parameter estimation of the stochastic actor-oriented model for network change (Ph.D. thesis), Università degli Studi di Milano-Bicocca.Google Scholar
Block, P., Stadtfeld, C., & Snijders, T. A. B. (2019). Forms of dependence: Comparing SAOMs and ERGMs from basic principles. Sociological Methods & Research, 48(1), 202–239.CrossRefGoogle Scholar
Block, P., Koskinen, J., Hollway, J. Steglich, C., & Stadtfeld, C. (2018). Change we can believe in: Comparing longitudinal network models on consistency, interpretability and predictive power. Social Networks, 52, 180–191.CrossRefGoogle Scholar
Boulesteix, A.-L., Lauer, S., & Eugster, M. J. A. (2013). A plea for neutral comparison studies in computational sciences. PLOS One, 8(4), e61562.CrossRefGoogle ScholarPubMed
Butts, C. T. (2008). A relational event framework for social action. Sociological Methodology, 38(1), 155–200.CrossRefGoogle Scholar
Butts, C. T. (2017). Comment: Actor orientation and relational event models. Sociological Methodology, 47(1), 47–56.Google Scholar
Chiba, D., Metternich, N. W., & Ward, M. D. (2015). Every story has a beginning, middle, and an end (but not always in that order): Predicting duration dynamics in a unified framework. Political Science Research and Methods, 3(3), 515–541.Google Scholar
Cranmer, S. J., & Desmarais, B. A. (2011). Inferential network analysis with exponential random graph models. Political Analysis, 19(1), 66–86.CrossRefGoogle Scholar
Cranmer, S. J., Desmarais, B. A., & Menninga, E. J. (2012). Complex dependencies in the alliance network. Conflict Management and Peace Science, 23(3), 279–313.CrossRefGoogle Scholar
Cranmer, S. J., Heinrich, T., & Desmarais, B. A. (2014). Reciprocity and the structural determinants of the international sanctions network. Social Networks, 36, 5–22.CrossRefGoogle Scholar
Cranmer, S. J., Leifeld, P., McClurg, S. D., & Rolfe, M. (2017). Navigating the range of statistical tools for inferential network analysis. American Journal of Political Science, 61(1), 237–251.CrossRefGoogle Scholar
Davis, J., & Goadrich, M. (2006). The relationship between precision-recall and ROC curves. In Proceedings of the 23rd international conference on machine learning (pp. 233–240). Pittsburgh, Pennsylvania, USA: ACM.Google Scholar
Desmarais, B. A., & Cranmer, S. J. (2010). Consistent confidence intervals for maximum pseudolikelihood estimators. In Proceedings of the neural information processing systems 2010 workshop on computational social science and the wisdom of crowds (pp. 1–4). December 10, 2010. Whistler, Canada: NIPS.Google Scholar
Desmarais, B. A., & Cranmer, S. J. (2012a). Micro-level interpretation of exponential random graph models with application to estuary networks. Policy Studies Journal, 40(3), 402–434.CrossRefGoogle Scholar
Desmarais, B. A., & Cranmer, S. J. (2012b). Statistical inference for valued-edge networks: The generalized exponential random graph model. PLOS One, 7(1), e30136.CrossRefGoogle Scholar
Desmarais, B. A., & Cranmer, S. J. (2012c). Statistical mechanics of networks: Estimation and uncertainty. PhysicaA: Statistical Mechanics and its Applications, 391(4), 1865–1876.CrossRefGoogle Scholar
Dow, M. M., Burton, M. L., White, D. R., & Reitz, K. P. (1984). Galton’s problem as network autocorrelation. American Ethnologist, 11(4), 754–770.CrossRefGoogle Scholar
Frank, O., & Strauss, D. (1986). Markov graphs. Journal of the American Statistical Association, 81(395), 832–842.CrossRefGoogle Scholar
Goodreau, S. M., Kitts, J., & Morris, M. (2008). Birds of a feather, or friend of a friend? Using exponential random graph models to investigate adolescent social networks. Demography, 46(1), 103–125.CrossRefGoogle Scholar
Handcock, M. S., Hunter, D. R., Butts, C. T., Goodreau, S. M., & Morris, M. (2008). statnet: Software tools for the representation, visualization, analysis and simulation of network data. Journal of Statistical Software, 24(1), 1–11.Google ScholarPubMed
Hanley, J. A., & McNeil, B. J. (1982). The meaning and use of the area under a receiver operating characteristic (ROC) curve. Radiology, 143(1), 29–36.CrossRefGoogle Scholar
Hanneke, S., Fu, W., & Xing, E. P. (2010). Discrete temporal models of social networks. The Electronic Journal of Statistics, 4, 585–605.CrossRefGoogle Scholar
Hoff, P. D., & Ward, M. D. (2004). Modeling dependencies in international relations networks. Political Analysis, 12(2), 160–175.CrossRefGoogle Scholar
Hoff, P. D., Raftery, A. E., & Handcock, M. S. (2002). Latent space approaches to social network analysis. Journal of the American Statistical Association, 97(460), 1090–1098.CrossRefGoogle Scholar
Holland, P. W., & Leinhardt, S. (1977). A dynamic model for social networks. Journal of Mathematical Sociology, 5(1), 5–20.CrossRefGoogle Scholar
Hunter, D. R., Handcock, M. S., Butts, C. T., Goodreau, S. M., & Morris, M. (2008). ergm: A package to fit, simulate and diagnose exponential-family models for networks. Journal ofStatistical Software, 24(3), 1–29.Google ScholarPubMed
Hunter, D. R., Goodreau, S. M., & Handcock, M. S. (2012). Goodness of fit of social network models. Journal of the American Statistical Association, 103(481), 248–258.Google Scholar
Iacobucci, D., & Wasserman, S. (1988). A general framework for the statistical analysis of sequential dyadic interaction data. Psychological Bulletin, 103(3), 379–390.Google ScholarPubMed
Kinne, B. J. (2013). Network dynamics and the evolution of international cooperation. American Political Science Review, 107(4), 766–785.CrossRefGoogle Scholar
Knecht, A. (2006). Networks and actor attributes in early adolescence. ICS Codebook 61. The Netherlands Research School ICS, Department of Sociology, Utrecht University, Utrecht.Google Scholar
Knecht, A. (2008). Friendship selection and friends’ influence. dynamics of networks and actor attributes in early adolescence (Ph.D. dissertation). University of Utrecht, Utrecht.Google Scholar
Koskinen, J., & Edling, C. (2012). Modelling the evolution of a bipartite network. Peer referral in interlocking directorates. Social Networks, 34(3), 309–322.CrossRefGoogle ScholarPubMed
Leifeld, P., Cranmer, S. J., & Desmarais, B. A. (2017). xergm: Extensions of exponential random graph models. R package version 1.8.2.Google Scholar
Leifeld, P., Cranmer, S. J., & Desmarais, B. A. (2018). Temporal exponential random graph models with btergm: Estimation and bootstrap confidence intervals. Journal of Statistical Software, 83(6), 1–36.Google Scholar
Lerner, J., Indlekofer, N., Nick, B., & Brandes, U. (2013). Conditional independence in dynamic networks. Journal of Mathematical Psychology, 57(6), 275–283.CrossRefGoogle Scholar
Lusher, D., Koskinen, J., & Robins, G. (2013). Exponential random graph models for social network analysis. New York, NY: Cambridge University Press.Google Scholar
Montgomery, J. M., Hollenbach, F. M., & Ward, M. D. (2015). Calibrating ensemble forecasting models with sparse data in the social sciences. International Journal of Forecasting, 31(3), 930–942.CrossRefGoogle Scholar
Morris, M., Handcock, M. S., & Hunter, D. R. (2008). Specification of exponential-family random graph models: Terms and computational aspects. Journal ofStatistical Software, 24(4), 1–4.Google ScholarPubMed
R Core Team. (2016). R: A language and environment for statistical computing. R Foundation for Statistical Computing, Vienna, Austria.Google Scholar
Ripley, R., Boitmanis, K., Snijders, T. A. B., & Schoenenberger, F. (2017a). RSiena: Siena—Simulation investigation for empirical network analysis. R package version 1.2–3.Google Scholar
Ripley, R. M., Snijders, T. A. B., Boda, Z., Vörös, A., & Preciado, P. (2017b). Manual for RSiena. Oxford: Department of Statistics, Nuffield College, University of Oxford.Google Scholar
Robins, G., & Pattison, P. (2001). Random graph models for temporal processes in social networks. Journal of Mathematical Sociology, 25(1), 5–41.CrossRefGoogle Scholar
Runger, G., & Wasserman, S. (1980). Longitudinal analysis of friendship networks. Social Networks, 2(2), 143–154.CrossRefGoogle Scholar
Sing, T., Sander, O., Beerenwinkel, N., & Lengauer, T. (2005). ROCR: Visualizing classifier performance in R. Bioinformatics, 21(20), 3940–3941.CrossRefGoogle Scholar
Snijders, T. A. B. (2001). The statistical evaluation of social network dynamics. Sociological Methodology, 31, 361–395.CrossRefGoogle Scholar
Snijders, T. A. B. (2005). Models for longitudinal network data. In Carrington, P. J., Scott, J., and Wasserman, S. (Eds.), Models and methods in social network analysis (Chap. 11, pp. 225–257). New York, NY: Cambridge University Press.Google Scholar
Snijders, T. A. B. (2017). Stochastic actor-oriented models for network dynamics. Annual Review of Statistics and its Application, 2017(4), 343–363.CrossRefGoogle Scholar
Snijders, T. A. B., & van Duijn, M. (1997). Simulation for statistical inference in dynamic network models. In Conte, R., Hegselmann, R., and Terna, P. (Eds.), Simulating social phenomena. Lecture Notes in Economics and Mathematical Systems (vol. 456, pp. 493–512). Berlin: Springer.CrossRefGoogle Scholar
Snijders, T. A. B., Steglich, C. E. G., & Schweinberger, M. (2007). Modeling the co-evolution of networks and behavior. In van Montfort, K., Oud, H., and Satorra, A. (Eds.), Longitudinal models in the behavioral and related sciences (pp. 41–71). Mahwah, NJ: Lawrence Erlbaum.Google Scholar
Snijders, T. A. B., Lomi, A., & Torló, V. J. (2013). A model for the multiplex dynamics of two-mode and one-mode networks, with an application to employment preference, friendship, and advice. Social Networks, 35(2), 265–276.CrossRefGoogle ScholarPubMed
Snijders, T. A. B., van de Bunt, G. G., & Steglich, C. E. G. (2010). Introduction to stochastic actor-based models for network dynamics. Social Networks, 32(1), 44–60.Google Scholar
Stadtfeld, C. (2013). NetSim: A social networks simulation tool in R. R package version 0.9. Retrieved January 7, 2018, from https://cran.r-project.org/package=NetSim. Package Vignette Retrieved January 31, 2019, from https://www.ethz.ch/content/dam/ethz/special-interest/gess/social-networks-dam/documents/jss_netsim.pdf.Google Scholar
Stadtfeld, C., & Block, P. (2017). Interactions, actors, and time: Dynamic network actor models for relational events. Sociological Science, 4, 318–352.Google Scholar
Stadtfeld, C., Hollway, J., & Block, P. (2017a). Dynamic network actor models: Investigating coordination ties through time. Sociological Methodology, 47(1), 1–40.Google Scholar
Stadtfeld, C., Hollway, J., & Block, P. (2017b). Rejoinder: DyNAMs and the grounds for actor-oriented network event models. Sociological Methodology, 47(1), 56–67.CrossRefGoogle Scholar
Steglich, C. E. G., Snijders, T. A. B., & Pearson, M. (2010). Dynamic networks and behavior: Separating selection from influence. Sociological Methodology, 40(1), 329–393.CrossRefGoogle Scholar
Veenstra, R., Dijkstra, J. K., Steglich, C. E. G., & van Zalk, M. H. W. (2013). Network-behavior dynamics. Journal of Research on Adolescence, 23(3), 399–412.Google Scholar
Wang, P., Robins, G., Pattison, P., & Lazega, E. (2013). Exponential random graph models for multilevel networks. Social Networks, 35(1), 96–115.CrossRefGoogle Scholar
Ward, M. D, Metternich, N. W, Dorff, C. L, Gallop, M., Hollenbach, F. M, Schultz, A., & Weschle, S. (2013). Learning from the past and stepping into the future: Toward a new generation of conflict prediction. International Studies Review, 15(4), 473–490.CrossRefGoogle Scholar
Warren, T. C. (2016). Modeling the coevolution of international and domestic institutions: Alliances, democracy, and the complex path to peace. Journal ofPeace Research, 53(3), 424–441.Google Scholar
Wasserman, S., & Iacobucci, D. (1986). Statistical analysis of discrete relational data. British Journal of Mathematical and Statistical Psychology, 39(1), 41–64.CrossRefGoogle ScholarPubMed
Wasserman, S., & Iacobucci, D. (1988). Sequential social network data. Psychometrika, 53(2), 261–282.CrossRefGoogle Scholar
Wasserman, S., & Pattison, P. (1996). Logit models and logistic regressions for social networks: I. An introduction to Markov graphs and p* models. Psychometrika, 61(3), 401–425.CrossRefGoogle Scholar