期刊
NETWORK SCIENCE
卷 7, 期 1, 页码 20-51出版社
CAMBRIDGE UNIV PRESS
DOI: 10.1017/nws.2018.26
关键词
p*; ERGM; TERGM; SAOM; temporal exponential random graph model; stochastic actor-oriented model; longitudinal networks; inferential network analysis; dynamic networks; statistical modeling
资金
- Zukunftskolleg at the University of Konstanz
- National Science Foundation [SES-1514750, SES-1461493, SES-1357622]
- Alexander von Humboldt Foundation
- 2015 Best Conference Paper Award by the American Political Science Association Organized Section on Political Networks
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.
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