We consider the problem of approximating the von Neumann entropy of a large, sparse,
symmetric positive semidefinite matrix A, defined as tr (f (A)) where f (x)=-x log x. After …

The von Neumann graph entropy is a measure of graph complexity based on the Laplacian
spectrum. It has recently found applications in various learning tasks driven by the …

The approximation of trace (f (Ω)), where f is a function of a symmetric matrix Ω, can be
challenging when Ω is exceedingly large. In such a case even the partial Lanczos …

This paper studies a graph entropy measure to characterize the structural complexity of
directed networks. Since the von Neumann entropy (VNE) has found applications in many …

The von Neumann graph entropy (VNGE) is a measure of graph complexity based on the
Laplacian spectrum. It has recently found applications in various learning tasks driven by …

Many applications in physics and network science require the computation of quantities
related to certain matrix functions. In many cases, a straightforward way to proceed is by …

Machine learning on graph-structured data has attracted high research interest due to the
emergence of Graph Neural Networks (GNNs). Most of the proposed GNNs are based on …

As the technological evolution of machine learning is accelerating nowadays, data plays a
vital role in building intelligent models, being able to simulate phenomena, predict values …

Dans ce travail, nous exploitons la variation de l'entropie de von Neumann de graphes
comme mesure de vulnérabilité en proposant une nouvelle forme approchée de cette …

This work explores how randomization can be exploited to deliver sophisticated algorithms
with provable bounds for:(i) The approximation of matrix functions, such as the log …