Graph neural networks are one of the main building blocks of AlphaFold, an artificial intelligence program developed by Google 's DeepMind for solving the protein folding problem in biology.

Nov 27, 2025 · Graph Neural Networks (GNNs) are deep learning models designed to work with graph-structured data, where information is represented as nodes and edges. Unlike traditional neural …

Sep 2, 2021 · A set of objects, and the connections between them, are naturally expressed as a graph. Researchers have developed neural networks that operate on graph data (called graph neural …

Jul 21, 2022 · Learn everything about Graph Neural Networks, including what GNNs are, the different types of graph neural networks, and what they're used for. Plus, learn how to build a Graph Neural …

Graph neural networks are a deep neural network architecture that represents data about entities and their relationships. They’re useful for real-world data mining, understanding social networks, …

CNNs and MLPs are specifically designed to handle non-Euclidean data, such as graphs and hyperbolic spaces, without any modifications.

Dec 2, 2025 · Researchers tried using Spectral Graph Theory (complex calculus from physics). It worked on paper, but couldn’t scale to massive graphs like Facebook’s social network or Google’s …

Graph neural networks (GNNs) compose layers of graph filters and point-wise non-linearities

Graph neural networks (GNNs) have recently grown in popularity in the field of artificial intelligence (AI) due to their unique ability to ingest relatively unstructured data types as input data.

Mar 7, 2024 · Graph neural networks (GNNs) are mathematical models that can learn functions over graphs and are a leading approach for building predictive models on graph-structured data.