Graph Neural Networks (GNNs) have recently drawn wide public attention due to the tremendous expressive power for graph analytics. Most existing GNN models convey messages among nodes solely based on ...

The expressive power of Graph Neural Networks (GNNs) has been studied extensively through the Weisfeiler-Leman (WL) graph isomorphism test. However, standard GNNs and the WL framework are inapplicable ...

Geometric scattering coefficients can be used for various downstream tasks, and in the paper we considered four of them, namely graph classification of social network datasets, graph classification ...

Abstract: Obtaining and handling geometric information is vital to monocular 3D object detection. The performance of current monocular 3D object detection models is largely limited by inaccurate depth ...

PyG (PyTorch Geometric) is a library built upon PyTorch to easily write and train Graph Neural Networks (GNNs) for a wide range of applications related to structured data. Whether you are a machine ...

Abstract: Discovery of knowledge from geometric graph databases is of particular importance in chemistry and biology, because chemical compounds and proteins are represented as graphs with 3D ...

Choose from Geometric Graph Backgrounds stock illustrations from iStock. Find high-quality royalty-free vector images that you won't find anywhere else.

Choose from Geometric Graph Background stock illustrations from iStock. Find high-quality royalty-free vector images that you won't find anywhere else.