Geometric Graph Neural Network

Geometric graph neural networks (GNNs) are a class of machine learning models designed to analyze data represented as graphs with embedded geometric information, such as molecular structures or 3D point clouds. Current research focuses on improving their ability to capture long-range interactions, enhancing generalization capabilities across different datasets, and developing more expressive architectures, including invariant and equivariant networks utilizing various basis representations (Cartesian, spherical). These advancements are significantly impacting fields like materials science, drug discovery, and robotics by enabling more accurate and efficient predictions of molecular properties, protein structure analysis, and multi-robot path planning.