Time-varying β-model for dynamic directed networks

We extend the well-known beta-model for directed graphs to dynamic network setting, where we observe snapshots of adjacency matrices at different time points. We propose a kernel-smoothed likelihood approach for estimating 2n time-varying parameters in a network with n nodes, from N snapshots. We establish consistency and asymptotic normality properties of our kernel-smoothed estimators as either n or N diverges. Our results contrast their counterparts in single-network analyses, where n -> infinity is invariantly required in asymptotic studies. We conduct comprehensive simulation studies that confirm our theory's prediction and illustrate the performance of our method from various angles. We apply our method to an email dataset and obtain meaningful results.