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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: [email protected])
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: [email protected])
*
*Corresponding author. Email: [email protected]
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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
Type
Original Article
Information
Network Science , Volume 7 , Issue 1 , March 2019 , pp. 20 - 51
Copyright
© Cambridge University Press 2019 

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