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Circular specifications and “predicting” with information from the future: Errors in the empirical SAOM–TERGM comparison of Leifeld & Cranmer

Published online by Cambridge University Press:  10 March 2022

Per Block Open the ORCID record for Per Block [Opens in a new window] ,
James Hollway Open the ORCID record for James Hollway [Opens in a new window] ,
Christoph Stadtfeld ,
Johan Koskinen  and
Tom Snijders Open the ORCID record for Tom Snijders [Opens in a new window]
Per Block*
Affiliation:
Department of Sociology, Leverhulme Centre for Demographic Science, and Nuffield College, University of Oxford, Oxford, UK
James Hollway
Affiliation:
The Graduate Institute Geneva, Geneva, Switzerland
Christoph Stadtfeld
Affiliation:
Chair of Social Networks, ETH Zürich, Zürich, Switzerland
Johan Koskinen
Affiliation:
Melbourne School of Psychological Sciences, University of Melbourne, Melbourne, Australia
Tom Snijders
Affiliation:
Nuffield College, University of Oxford, Oxford, UK Department of Sociology, University of Groningen, Groningen, The Netherlands
*
*Corresponding author. Email: [email protected]
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Abstract

We review the empirical comparison of Stochastic Actor-oriented Models (SAOMs) and Temporal Exponential Random Graph Models (TERGMs) by Leifeld & Cranmer in this journal [Network Science 7(1):20–51, 2019]. When specifying their TERGM, they use exogenous nodal attributes calculated from the outcome networks’ observed degrees instead of endogenous ERGM equivalents of structural effects as used in the SAOM. This turns the modeled endogeneity into circularity and obtained results are tautological. In consequence, their out-of-sample predictions using TERGMs are based on out-of-sample information and thereby predict the future using observations from the future. Thus, their analysis rests on erroneous model specifications that invalidate the article’s conclusions. Finally, beyond these specific points, we argue that their evaluation metric—tie-level predictive accuracy—is unsuited for the task of comparing model performance.

Keywords

SAOMERGMTERGMstatistical modelingdynamic networksinferential network analysislongitudinal networks
Type
Commentary
Information
Network Science , Volume 10 , Issue 1 , March 2022 , pp. 3 - 14
Copyright
© The Author(s), 2022. Published by Cambridge University Press

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