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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: [email protected] Institute of Political Science, University of Bern, CH-3012 Bern, Switzerland
*
*Email: [email protected]
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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
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. 

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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

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