Graphs naturally appear in numerous application domains, ranging from social analysis,
bioinformatics to computer vision. The unique capability of graphs enables capturing the …

Compelling experimental evidence suggests the existence of new physics beyond the well-
established and tested standard model of particle physics. Various current and upcoming …

Graph Neural Networks (GNNs) have recently become increasingly popular due to their
ability to learn complex systems of relations or interactions arising in a broad spectrum of …

Graph machine learning (GML) is receiving growing interest within the pharmaceutical and
biotechnology industries for its ability to model biomolecular structures, the functional …

Combinatorial optimization problems are pervasive across science and industry. Modern
deep learning tools are poised to solve these problems at unprecedented scales, but a …

Particle physics is a branch of science aiming at discovering the fundamental laws of matter
and forces. Graph neural networks are trainable functions which operate on graphs—sets of …

Social media are nowadays one of the main news sources for millions of people around the
globe due to their low cost, easy access and rapid dissemination. This however comes at the …

The ability to detect and count certain substructures in graphs is important for solving many
tasks on graph-structured data, especially in the contexts of computational chemistry and …

Geometric deep learning, a novel class of machine learning algorithms extending classical
deep learning architectures to non-Euclidean structured data such as manifolds and graphs …

Intrinsic graph convolution operators with differentiable kernel functions play a crucial role in
analyzing 3D shape meshes. In this paper, we present a fast and efficient intrinsic mesh …