Title: Mixed-effect time-varying stochastic blockmodel and application in brain connectivity analysis
Authors: Lexin Li - University of California Berkeley (United States) [presenting]
Abstract: 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. 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.