Understanding Convolutions on Graphs
This article is one of two Distill publications about graph neural networks. Take a look at A Gentle Introduction to Graph Neural Networks for a companion view on many things graph and neural network related. Many systems and interactions - social networks, molecules, organizations, citations, physical models, transactions - can be represented quite naturally as graphs. How can we reason about and
A Gentle Introduction to Graph Neural Networks
Neural networks have been adapted to leverage the structure and properties of graphs. We explore the components needed for building a graph neural network - and motivate the design choices behind them. Hover over a node in the diagram below to see how it accumulates information from nodes around it through the layers of the network. Authors Affiliations Benjamin Sanchez-Lengeling Google Research E
Distill — Latest articles about machine learning
Understanding Convolutions on Graphs Ameya Daigavane, Balaraman Ravindran, and Gaurav Aggarwal Understanding the building blocks and design choices of graph neural networks. A Gentle Introduction to Graph Neural Networks Benjamin Sanchez-Lengeling, Emily Reif, Adam Pearce, and Alexander B. Wiltschko What components are needed for building learning algorithms that leverage the structure and propert
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