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 …

While advances in computing resources have made processing enormous amounts of data
possible, human ability to identify patterns in such data has not scaled accordingly. Efficient …

Message passing graph neural networks (GNNs) are a popular learning architectures for
graph-structured data. However, one problem GNNs experience is oversquashing, where a …

This survey describes probabilistic algorithms for linear algebraic computations, such as
factorizing matrices and solving linear systems. It focuses on techniques that have a proven …

Previously proposed quantum algorithms for solving linear systems of equations cannot be
implemented in the near term due to the required circuit depth. Here, we propose a hybrid …

Graph Neural Networks (GNNs) have achieved a lot of success on graph-structured data.
However, it is observed that the performance of graph neural networks does not improve as …

Training on large-scale graphs has achieved remarkable results in graph representation
learning, but its cost and storage have attracted increasing concerns. Existing graph …

A number of problems can be formulated as prediction on graph-structured data. In this
work, we generalize the convolution operator from regular grids to arbitrary graphs while …

This article shows how to solve linear programs of the form min Ax= b, x≥ 0 c⊤ x with n
variables in time O*((n ω+ n 2.5− α/2+ n 2+ 1/6) log (n/δ)), where ω is the exponent of matrix …

This survey highlights the recent advances in algorithms for numerical linear algebra that
have come from the technique of linear sketching, whereby given a matrix, one first …