The stochastic block model (SBM) is a random graph model with planted clusters. It is widely
employed as a canonical model to study clustering and community detection, and provides …

We consider the high-dimensional inference problem where the signal is a low-rank
symmetric matrix which is corrupted by an additive Gaussian noise. Given a probabilistic …

Inference problems with conjectured statistical-computational gaps are ubiquitous
throughout modern statistics, computer science, statistical physics and discrete probability …

This article is an extended version of previous work of Lesieur et al (2015 IEEE Int. Symp. on
Information Theory Proc. pp 1635–9 and 2015 53rd Annual Allerton Conf. on …

We consider increasingly complex models of matrix denoising and dictionary learning in the
Bayes-optimal setting, in the challenging regime where the matrices to infer have a rank …

This paper investigates fundamental limits of exact recovery in the general-uniform
hypergraph stochastic block model (-HSBM), wherein nodes are partitioned into disjoint …

Many inference problems undergo phase transitions as a function of the signal-to-noise
ratio, a prominent example of this phenomenon being found in the stochastic block model …

We consider the problem of recovering the community structure in the stochastic block
model with two communities. We aim to describe the mutual information between the …

In this paper, we introduce new, easily implementable designs for drawing causal inference
from randomized experiments on networks with interference. Inspired by the idea of …

We study the weak recovery problem on the $ r $-uniform hypergraph stochastic block
model ($ r $-HSBM) with two balanced communities. In HSBM a random graph is …