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 …

Statistical network modelling has focused on representing the graph as a discrete structure,
namely the adjacency matrix. When assuming exchangeability of this array—which can aid …

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 …

Spectral embedding is a procedure which can be used to obtain vector representations of
the nodes of a graph. This paper proposes a generalisation of the latent position network …

We consider the problem of estimating community memberships of nodes in a network,
where every node is associated with a vector determining its degree of membership in each …

Community detection on mixture multilayer networks via regularized tensor decomposition
Page 1 The Annals of Statistics 2021, Vol. 49, No. 6, 3181–3205 https://doi.org/10.1214/21-AOS2079 …

Clustering is concerned with coherently grouping observations without any explicit concept
of true groupings. Spectral graph clustering—clustering the vertices of a graph based on …

Two-sample hypothesis testing for random graphs arises naturally in neuroscience, social
networks, and machine learning. In this article, we consider a semiparametric problem of two …

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 …