  Researchers interested in statistically modeling network data have a
well-established and quickly growing set of approaches from which to choose.
Several of these methods have been regularly applied in research on political
networks, while others have yet to permeate the field. Here, we review the most
prominent methods of inferential network analysis---both for cross-sectionally
and longitudinally observed networks including (temporal) exponential random
graph models, latent space models, the quadratic assignment procedure, and
stochastic actor oriented models. For each method, we summarize its analytic
form, identify prominent published applications in political science and
discuss computational considerations. We conclude with a set of guidelines for
selecting a method for a given application.
