A model for network panel data is discussed, based on the assumption that theobserved data are discrete observations of a continuous-time Markov process onthe space of all directed graphs on a given node set, in which changes in tievariables are independent conditional on the current graph. The model for tiechanges is parametric and designed for applications to social network analysis,where the network dynamics can be interpreted as being generated by choicesmade by the social actors represented by the nodes of the graph. An algorithmfor calculating the Maximum Likelihood estimator is presented, based on dataaugmentation and stochastic approximation. An application to an evolvingfriendship network is given and a small simulation study is presented whichsuggests that for small data sets the Maximum Likelihood estimator is moreefficient than the earlier proposed Method of Moments estimator.
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