Many systems of scientific interest can be naturally thought of as networks of evolving relationships, for instance, linkages between neurons in the brain, computers on the internet, or adolescents in a high school. The last 20 years or so have seen rapid progress in methods for the empirical study of network evolution. The Stochastic Actor-Oriented Model (SAOM) framework is one of the most popular such approaches in the behavioral sciences, and is a particularly natural way to represent systems in which tie formation and maintenance depend on characteristics of some set of potentially interconnected nodes, and where also the interconnections, once formed, can “feed back� and affect nodal characteristics. In Part 1 of this presentation, I will describe the basic structure of a SAOM as a set of interrelated continuous-time Markov processes, and show how transition probabilities can be specified as functions of linkage and nodal data. In Part 2, an empirical application will be given, where the goal was to detect indirect (“spillover�) effects on alcohol use of an intervention with a subset of heavy drinkers in a college freshmen class.