A1240
Title: Euclidean mirrors and first-order changepoints in network time series
Authors: Zachary Lubberts - University of Virginia (United States) [presenting]
Abstract: A model for a network time series is described, whose evolution is governed by an underlying stochastic process known as the latent position process, in which network evolution can be represented in Euclidean space by a curve, called the Euclidean mirror. The notion of a first-order changepoint for a time series of networks is defined, and a family of latent position process networks with underlying first-order changepoints is constructed. A spectral estimate of the associated Euclidean mirror is proven to localize these changepoints, even when the graph distribution evolves continuously but at a rate that changes. Simulated and real data examples on brain organoid networks show that this localization captures empirically significant shifts in network evolution.
