The random dot product graph (RDPG) is an independent-edge random graph that is
analytically tractable and, simultaneously, either encompasses or can successfully …

The data science of networks is a rapidly developing field with myriad applications. In
neuroscience, the brain is commonly modeled as a connectome, a network of nodes …

The development of models and methodology for the analysis of data from multiple
heterogeneous networks is of importance both in statistical network theory and across a …

The singular value matrix decomposition plays a ubiquitous role throughout statistics and
related fields. Myriad applications including clustering, classification, and dimensionality …

We prove a central limit theorem for the components of the eigenvectors corresponding to
the d largest eigenvalues of the normalized Laplacian matrix of a finite dimensional random …

Vertex clustering in a stochastic blockmodel graph has wide applicability and has been the
subject of extensive research. In this paper, we provide a short proof that the adjacency …

While statistical analysis of a single network has received a lot of attention in recent years,
with a focus on social networks, analysis of a sample of networks presents its own …

We consider the problem of testing whether two independent finite-dimensional random dot
product graphs have generating latent positions that are drawn from the same distribution, or …

The study of networks leads to a wide range of high-dimensional inference problems. In
many practical applications, one needs to draw inference from one or few large sparse …

A core problem in statistical network analysis is to develop network analogues of classical
techniques. The problem of bootstrapping network data stands out as especially …