We study the problem of detecting latent geometric structure in random graphs. To this end,
we consider the soft high-dimensional random geometric graph G (n, p, d, q), where each of …

This article studies the recovery of graphons when they are convolution kernels on compact
(symmetric) metric spaces. This case is of particular interest since it covers the situation …

We are given the adjacency matrix of a geometric graph and the task of recovering the latent
positions. We study one of the most popular approaches which consists in using the graph …

Let X v for v∈ V be a family of n iid uniform points in the square. Suppose first that we are
given the random geometric graph, where vertices u and v are adjacent when the Euclidean …

Let G n G _n be a random geometric graph, and then for q, p∈ 0, 1) q, p ∈\left 0, 1\right) we
construct a (q, p)\left (q, p\right)‐perturbed noisy random geometric graph G nq, p G _n^ q, p …

Networks with node covariates are commonplace: for example, people in a social network
have interests, or product preferences, etc. If we know the covariates for some nodes, can …

Embedding graphs in a geographical or latent space, ie inferring locations for vertices in
Euclidean space or on a smooth manifold or submanifold, is a common task in network …

Random graphs are mathematical models that have applications in a wide range of
domains. We study the following model where one adds Erd\H {o} s--R\'enyi (ER) type …

Embedding graphs in a geographical or latent space, ie inferring locations for vertices in
Euclidean space or on a smooth manifold or submanifold, is a common task in network …

Random graphs are canonical models for network data in various disciplines, including
information technology, social studies, artificial intelligence, and biological sciences. These …