Time-varying degree-corrected stochastic block models

Recent interest has emerged in community detection for dynamic networks which are observed along a trajectory of points in time. In this talk, we present a time-varying degree-corrected stochastic block model to fit a dynamic network which allows evolving heterogeneity in the degrees of nodes within a community over time. Considering the influence of the varying time window on the aggregation of network information from different time points, in the parameter estimation, we propose a smoothing-based method to recover time-varying degree parameters and communities.
In particular we provide rates of consistency of our smoothed estimators for degree parameters and communities using a time-localised profile-likelihood approach. We illustrate our method by some comparative simulation studies and an application to a real data set.
This is joint work with Mengxue Li and Eugen Pircalabelu (UCLouvain).