Community detection in networks is one of the most popular topics of modern network
science. Communities, or clusters, are usually groups of vertices having higher probability of …

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 …

Graph Neural Networks (GNNs) have achieved state-of-the-art results on many graph
analysis tasks such as node classification and link prediction. However, important …

We consider the recovery of a low rank real-valued matrix M given a subset of noisy discrete
(or quantized) measurements. Such problems arise in several applications such as …

Discovering and characterizing the large-scale topological features in empirical networks
are crucial steps in understanding how complex systems function. However, most existing …

We consider three distinct and well-studied problems concerning network structure:
community detection by modularity maximization, community detection by statistical …

Decelle et al.[1] conjectured the existence of a sharp threshold on model parameters for
community detection in sparse random graphs drawn from the stochastic block model …

Spectral algorithms are classic approaches to clustering and community detection in
networks. However, for sparse networks the standard versions of these algorithms are …

We study a random graph model called the “stochastic block model” in statistics and the
“planted partition model” in theoretical computer science. In its simplest form, this is a …

We present a simple and flexible method to prove consistency of semidefinite optimization
problems on random graphs. The method is based on Grothendieck's inequality. Unlike the …