A0764
Title: Learning cross-layer dependence structure in multilayer networks
Authors: Jiaheng Li - Florida State University (United States) [presenting]
Jonathan Stewart - Rice University (United States)
Abstract: Multilayer networks are a network data structure in which elements within a population have multiple modes of interaction or relation, each represented by distinct networks called layers. We propose a novel class of separable multilayer network models to capture cross-layer dependencies in multilayer networks, enabling the analysis of how interactions in one or more layers may influence interactions in other layers. Our approach separates the network formation process from the layer formation process, and is able to extend existing single-layer network models to a multilayer network model that accommodates cross-layer dependence. We establish non-asymptotic and minimax-optimal error bounds for maximum likelihood estimators and demonstrate the convergence rate in scenarios of increasing parameter dimension. Additionally, we establish non-asymptotic error bounds for multivariate normal approximations and propose a model selection method that controls the false discovery rate. Simulation studies show that our framework and method perform well in realistic settings which might be encountered in applications. Lastly, we illustrate the utility of our method through an application to the Lazega lawyers network.
