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

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  • Brandenberger, Laurence

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.

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  • Brandenberger, Laurence, 2019. "Predicting Network Events to Assess Goodness of Fit of Relational Event Models," Political Analysis, Cambridge University Press, vol. 27(4), pages 556-571, October.
    • Handle: RePEc:cup:polals:v:27:y:2019:i:04:p:556-571_00
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    Cited by:

    1. Philip Leifeld, 2020. "Policy Debates and Discourse Network Analysis: A Research Agenda," Politics and Governance, Cogitatio Press, vol. 8(2), pages 180-183.

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