Stochastic block models are widely used to analyze random networks where nodes are clustered based on similar connecting probabilities. In many applications, the connecting intensities are subject to node-wise time shifts. Failing to account for the unknown time shifts may result in unidentifiability or misclustering. In this project, we propose a stochastic block model incorporating unknown time shifts in dynamic networks. We establish the conditions that guarantee the identifiability of cluster memberships of nodes and representative connecting intensities across clusters. Using methods for shape invariant models, we propose computationally efficient semiparametric estimation procedures to simultaneously estimate time shifts, cluster memberships, and connecting intensities. We illustrate the performance of the proposed procedures via extensive simulation experiments. We further apply the proposed method to a neural data set to reveal distinct roles of neurons during motor circuit maturation in zebrafish.
Speaker: Shizhe Chen, PhD, Assistant Professor of Statistics, UC Davis
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