Graph neural network - Wikipedia
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.
A Gentle Introduction to Graph Neural Networks - Distill
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 …
What are Graph Neural Networks? - GeeksforGeeks
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 …
What is a Graph Neural Network | IBM
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 …
A Comprehensive Introduction to Graph Neural Networks (GNNs)
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 …
A Beginner’s Guide to Graph Neural Networks
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 …
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?
Graph neural networks - Nature Reviews Methods Primers
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.