Graph neural networks (GNN) are specialized artificial neural networks that are designed for tasks whose inputs are graphs. [1] [2] [3] [4] [5] One prominent example is molecular drug design. [6] …

May 30, 2024 · Graph Neural Networks (GNNs) are a class of artificial neural networks designed to process data that can be represented as graphs. Unlike traditional neural networks that …

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 …

Mar 4, 2025 · Graph Neural Networks (GNNs) are a class of neural networks designed specifically to work with graph-structured data. They’re used to learn patterns and relationships between …

Jan 3, 2023 · In this blog post, we cover the basics of graph machine learning. We first study what graphs are, why they are used, and how best to represent them. We then cover briefly how …

Sep 2, 2021 · A Gentle Introduction to Graph Neural Networks. Neural networks have been adapted to leverage the structure and properties of graphs. We explore the components …

Mar 26, 2025 · Graph neural networks (GNNs) can be pictured as a special class of neural network models where data are structured as graphs — both training data used to train the …

Feb 19, 2025 · Graph neural networks (GNNs) are a deep neural network architecture that is popular both in practical applications and cutting-edge machine learning research. They use a …

Apr 4, 2023 · At its core, Graph Machine Learning (GML) is the application of machine learning to graphs specifically for predictive and prescriptive tasks. GML has a variety of use cases …

Dec 27, 2024 · This survey introduces graph neural networks through the encoder-decoder framework and provides examples of decoders for a range of graph analytic tasks. It uses …