Mixed-Effect Time-Varying Network Model and Application in Brain Connectivity Analysis

Research output: Contribution to journalArticle


Time-varying networks are fast emerging in a wide range of scientific and business applications. Most existing dynamic network models are limited to a single-subject and discrete-time setting. In this article, we propose a mixed-effect network model that characterizes the continuous time-varying behavior of the network at the population level, meanwhile taking into account both the individual subject variability as well as the prior module information. We develop a multistep optimization procedure for a constrained likelihood estimation and derive the associated asymptotic properties. We demonstrate the effectiveness of our method through both simulations and an application to a study of brain development in youth. Supplementary materials for this article are available online.

Original languageEnglish (US)
JournalJournal of the American Statistical Association
StateAccepted/In press - Jan 1 2019



  • Brain connectivity analysis
  • Fused lasso
  • Generalized linear mixed-effect model
  • Stochastic blockmodel
  • Time-varying network

ASJC Scopus subject areas

  • Statistics and Probability
  • Statistics, Probability and Uncertainty

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