  A model for network panel data is discussed, based on the assumption that the
observed data are discrete observations of a continuous-time Markov process on
the space of all directed graphs on a given node set, in which changes in tie
variables are independent conditional on the current graph. The model for tie
changes is parametric and designed for applications to social network analysis,
where the network dynamics can be interpreted as being generated by choices
made by the social actors represented by the nodes of the graph. An algorithm
for calculating the Maximum Likelihood estimator is presented, based on data
augmentation and stochastic approximation. An application to an evolving
friendship network is given and a small simulation study is presented which
suggests that for small data sets the Maximum Likelihood estimator is more
efficient than the earlier proposed Method of Moments estimator.
