This paper investigates a change-point estimation problem in the context of highdimensional Markov Random Field models. Change-points represent a key feature in many dynamically evolving network structures. The change-point estimate is obtained by maximizing a profile penalized pseudo-likelihood function under a sparsity assumption. We also derive a tight bound for the estimate, up to a logarithmic factor, even in settings where the number of possible edges in the network far exceeds the sample size. The performance of the proposed estimator is evaluated on synthetic data sets and is also used to explore voting patterns in the US Senate in the 1979-2012 period. keywords: Change-point analysis, High-dimensional inference, Markov random fields, Network analysis, Profile Pseudo-likelihood.
REF considered the detection of a change point in a sequence of evolving Markov random fields.
Change-Point Estimation in High-Dimensional Markov Random Field Models
Journal of the Royal Statistical Society : Series B, Volume 79, Issue 4, Pages 1187--1206, 2017
Mathematics Medicine