Dr. Jesús D. Arroyo Relión | Texas A&M University 

Abstract: Modern network datasets are often composed of multiple layers, such as different views, time-varying observations or independent sample units. These data require flexible and tractable models and methods capable of aggregating information across the networks. To that end, this talk considers the community detection problem under the multilayer degree-corrected stochastic blockmodel. We propose a spectral clustering algorithm and demonstrate that its misclustering error rate improves exponentially with multiple network realizations, even in the presence of significant layer heterogeneity. The methodology is illustrated in a case study of US airport data, where we identify meaningful community structure and trends influenced by pandemic impacts on travel. This is joint work with Joshua Agterberg and Zachary Lubberts.