Hostname: page-component-7f64f4797f-d87pz Total loading time: 0 Render date: 2025-11-05T09:02:53.480Z Has data issue: false hasContentIssue false
  • English
  • Français

Predicting Network Events to Assess Goodness of Fit of Relational Event Models

Published online by Cambridge University Press:  29 April 2019

Laurence Brandenberger Open the ORCID record for Laurence Brandenberger [Opens in a new window]
Laurence Brandenberger*
Affiliation:
Chair of Systems Design, ETH Zurich, Weinbergstr. 56/ 58, CH-8092 Zurich, Switzerland. Email: lbrandenberger@ethz.ch Institute of Political Science, University of Bern, CH-3012 Bern, Switzerland
*
*Email: lbrandenberger@ethz.ch
Get access Rights & Permissions [Opens in a new window]

Abstract

Relational event models are becoming increasingly popular in modeling temporal dynamics of social networks. Due to their nature of combining survival analysis with network model terms, standard methods of assessing model fit are not suitable to determine if the models are specified sufficiently to prevent biased estimates. This paper tackles this problem by presenting a simple procedure for model-based simulations of relational events. Predictions are made based on survival probabilities and can be used to simulate new event sequences. Comparing these simulated event sequences to the original event sequence allows for in depth model comparisons (including parameter as well as model specifications) and testing of whether the model can replicate network characteristics sufficiently to allow for unbiased estimates.

Keywords

dynamic networkgoodness of fitpredictionrelational event model

Information

Type
Articles
Information
Political Analysis , Volume 27 , Issue 4 , October 2019 , pp. 556 - 571
Copyright
Copyright © The Author(s) 2019. Published by Cambridge University Press on behalf of the Society for Political Methodology. 

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

Footnotes

Author’s note: The author would like to thank Philip Leifeld for helpful comments and conversations as well as participants of the conference panel ‘Modeling Network Dynamics II: Time-stamped Network Data’ at the Third European Conference on Social Networks in Mainz (September, 2017) for helpful questions and comments. The author would also like to thank two anonymous reviewers and the editor for their valuable comments. LB carried out parts of this research while at the Swiss Federal Institute of Aquatic Science and Technology (Eawag).

Contributing Editor: Jeff Gill

References

Andersen, P. K., and Gill, R. D.. 1982. “Cox’s Regression Model for Counting Processes: A Large Sample Study.” The Annals of Statistics 10:1100–1120.Google Scholar
Brandenberger, L.2018a. “Replication Files for: Predicting Network Events to Assess Goodness of Fit of Relational Event Models.” https://doi.org/10.7910/DVN/GM5SYQ, Harvard Dataverse, V1.Google Scholar
Brandenberger, L. 2018b. “Trading Favors - Examining the Temporal Dynamics of Reciprocity in Congressional Collaborations Using Relational Event Models.” Social Networks 54:238–253.Google Scholar
Brandes, U., Lerner, J., and Snijders, T. A.. 2009. “Networks Evolving Step by Step: Statistical Analysis of Dyadic Event Data.” 2009 International Conference on Advances in Social Network Analysis and Mining, 200–205. Piscataway, NJ: Institute of Electrical and Electronics Engineers.Google Scholar
Butts, C. T. 2008. “A Relational Event Framework for Social Action.” Sociological Methodology 38(1):155–200.Google Scholar
Cox, D. R., and Oakes, D.. 1984. Analysis of Survival Data . London: Chapman and Hall.Google Scholar
Cranmer, S. J., Leifeld, P., McClurg, S. D., and Rolfe, M.. 2017. “Navigating the Range of Statistical Tools for Inferential Network Analysis.” American Journal of Political Science 61(1):237–251.Google Scholar
Davis, J., and Goadrich, M.. 2006. “The Relationship Between Precision-Recall and Roc Curves.” In Proceedings of the 23rd International Conference on Machine Learning , 233–240. ACM.Google Scholar
Desmarais, B. A., Moscardelli, V. G., Schaffner, B. F., and Kowal, M. S.. 2015. “Measuring Legislative Collaboration: The Senate Press Events Network.” Social Networks 40:43–54.Google Scholar
DuBois, C., Butts, C., and Smyth, P.. 2013. “Stochastic Blockmodeling of Relational Event Dynamics.” In Proceedings of the Sixteenth International Conference on Artificial Intelligence and Statistics , 238–246. Available at http://proceedings.mlr.press/v31/dubois13a.pdf.Google Scholar
Hunter, D. R., Goodreau, S. M., and Handcock, M. S.. 2008. “Goodness of Fit of Social Network models.” Journal of the American Statistical Association 103(481):248–258.Google Scholar
Hunter, D. R., Handcock, M. S., Butts, C. T., Goodreau, S. M., and Morris, M.. 2008. “Ergm: A Package to Fit, Simulate and Diagnose Exponential-Family Models for Networks.” Journal of Statistical Software 24(3):nihpa54860.Google Scholar
Kitts, J. A., Lomi, A., Mascia, D., Pallotti, F., and Quintane, E. et al. . 2016. “Investigating the Temporal Dynamics of Inter-Organizational Exchange: Patient Transfers Among Italian Hospitals.” American Journal of Sociology 123(3):850–910.Google Scholar
Leifeld, P. 2016. Policy Debates as Dynamic Networks: German Pension Politics and Privatization Discourse . Frankfurt am Main: Campus.Google Scholar
Leifeld, P. 2017. “Discourse Network Analysis: Policy Debates as Dynamic Networks.” In The Oxford Handbook of Political Networks, Chapter 12 , edited by Victor, J. N., Lubell, M. N., and Montgomery, A. H., 301–326. Oxford: Oxford University Press.Google Scholar
Leifeld, P., and Brandenberger, L.. 2019. “Endogenous Coalition Formation in Policy Debates.” Preprint, arXiv:1904.05327.Google Scholar
Leifeld, P., Cranmer, S. J., and 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., Bussmann, M., Snijders, T. A., and Brandes, U.. 2013. “Modeling Frequency and Type of Interaction in Event Networks.” Corvinus Journal of Sociology and Social Policy 4(1):3–32.Google Scholar
Lerner, J., Indlekofer, N., Nick, B., and Brandes, U.. 2013. “Conditional Independence in Dynamic Networks.” Journal of Mathematical Psychology 57(6):275–283.Google Scholar
Quintane, E., Conaldi, G., Tonellato, M., and Lomi, A.. 2014. “Modeling Relational Events. A Case Study on an Open Source Software Project.” Organizational Research Methods 17(1):23–50.Google Scholar
Robins, G., Pattison, P., and Woolcock, J.. 2005. “Small and Other Worlds: Global Network Structures From Local Processes.” American Journal of Sociology 110(4):894–936.Google Scholar
Sabatier, P. A., and Jenkins-Smith, H.. 1993. Policy Change and Learning: An Advocacy Coalition Approach . Boulder: Westview Press.Google Scholar
Welbers, K., and de Nooy, W.. 2014. “Stylistic Accommodation on an Internet Forum as bonding: Do Posters Adapt to the Style of Their Peers?.” American Behavioral Scientist 58(10):1361–1375.Google Scholar
Supplementary material: File

Brandenberger supplementary material

Brandenberger supplementary material 1

Download Brandenberger supplementary material(File)
File 303.5 KB