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

TL;DR

This paper introduces a novel mixed-effect continuous-time stochastic blockmodel for analyzing time-varying networks across multiple subjects, with applications in brain connectivity development.

Contribution

It presents a new multi-subject, continuous-time network model that accounts for individual variability and provides an estimation procedure with proven asymptotic properties.

Findings

Effective in simulations

Successfully applied to brain development data

Captures population and individual network dynamics

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. 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.