Graph neural networks (GNNs) have emerged as a powerful tool for graph classification and
representation learning. However, GNNs tend to suffer from over-smoothing problems and …

Efficient numerical linear algebra is a core ingredient in many applications across almost all
scientific and industrial disciplines. With this survey we want to illustrate that numerical linear …

Graph convolutional networks (GCNs) are powerful tools for graph-structured data.
However, they have been recently shown to be vulnerable to topological attacks. To …

Gaussian mixture block models are distributions over graphs that strive to model modern
networks: to generate a graph from such a model, we associate each vertex $ i $ with a …

We consider the community detection problem in a sparse-uniform hypergraph, assuming
that is generated according to the Hypergraph Stochastic Block Model (HSBM). We prove …

We propose a new, efficiently solvable hierarchy of semidefinite programming relaxations for
inference problems. As test cases, we consider the problem of community detection in block …

We consider the community detection problem in sparse random hypergraphs. Angelini et
al. in [6] conjectured the existence of a sharp threshold on model parameters for community …

We study robust community detection in the context of node-corrupted stochastic block
model, where an adversary can arbitrarily modify all the edges incident to a fraction of the n …

Community detection in random graphs or hypergraphs is an interesting fundamental
problem in statistics, machine learning and computer vision. When the hypergraphs are …

The present work is concerned with community detection. Specifically, we consider a
random graph drawn according to the stochastic block model: its vertex set is partitioned into …