Spectral methods have emerged as a simple yet surprisingly effective approach for
extracting information from massive, noisy and incomplete data. In a nutshell, spectral …

Abstract The Random Geometric Graph (RGG) is a random graph model for network data
with an underlying spatial representation. Geometry endows RGGs with a rich dependence …

We consider a stochastic bandit problem with a possibly infinite number of arms. We write $
p^* $ for the proportion of optimal arms and $\Delta $ for the minimal mean-gap between …

This article considers spectral community detection in the regime of sparse networks with
heterogeneous degree distributions, for which we devise an algorithm to efficiently retrieve …

For a fixed symmetric matrix A and symmetric perturbation E we develop purely deterministic
bounds on how invariant subspaces of A and A+E can differ when measured by a suitable …

In network analysis, it is common to work with a collection of graphs that exhibit
heterogeneity. For example, neuroimaging data from patient cohorts are increasingly …

Embedding the nodes of a large network into an Euclidean space is a common objective in
modern machine learning, with a variety of tools available. These embeddings can then be …

To characterize the location (mean, median) of a set of graphs, one needs a notion of
centrality that is adapted to metric spaces, since graph sets are not Euclidean spaces. A …

We study the properties of conformal prediction for network data under various sampling
mechanisms that commonly arise in practice but often result in a non-representative sample …

The paper introduces a Signed Generalized Random Dot Product Graph (SGRDPG) model,
which is a variant of the Generalized Random Dot Product Graph (GRDPG), where, in …