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.

Sep 2, 2021 · Researchers have developed neural networks that operate on graph data (called graph neural networks, or GNNs) for over a decade. Recent developments have increased their capabilities …

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 …

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, …

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 …

6 days ago · 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 web index.

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

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

What are the fundamental motivations and mechanics that drive Graph Neural Networks, what are the diferent variants, and what are their applications?

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.