  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.
