  Time-varying networks are fast emerging in a wide range of scientific and
business disciplines. Most existing dynamic network models are limited to a
single-subject and discrete-time setting. In this article, we propose a
mixed-effect multi-subject continuous-time stochastic blockmodel that
characterizes the time-varying behavior of the network at the population level,
meanwhile taking into account individual subject variability. We develop a
multi-step optimization procedure for a constrained stochastic blockmodel
estimation, and derive the asymptotic property of the estimator. We demonstrate
the effectiveness of our method through both simulations and an application to
a study of brain development in youth.
