Change Detection in Core-Periphery Networks: A Case Study on Detecting Financial Crises in the Interbank Market

Abstract

We develop and present a new methodology to detect regime changes within a sequence of sparse networks that have overlapping and evolving community structure. The core of the methodology is a non-negative matrix factorization that maximizes a Poisson likelihood subject to a penalty that accounts for sparsity in the network. By fitting the factorization model over a rolling window with a fast numerical optimization algorithm, change detection is accomplished by statistical monitoring of the matrix factors' evolution. Using synthetic and real financial interbank lending networks, we demonstrate that the proposed methodology compares favorably with alternative techniques for on-the-go network change detection.