Understanding Convolutions on Graphs

Understanding the building blocks and design choices of graph neural networks.

Understanding Convolutions on Graphs Understanding Convolutions on Graphs Distill Understanding Convolutions on Graphs Understanding the building blocks and design choices of graph neural networks. Authors Affiliations Ameya Daigavane Google Research Balaraman Ravindran Google Research Gaurav Aggarwal Google Research Published Sept. 2, 2021 DOI 10.23915/distill.00032 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 inte

saved by

• Stephen Lu
• Emma Guo
• Aryan Garg

related reading

• A Gentle Introduction to Graph Neural Networksdistill.pub
• Graph Convolutional Networks | Thomas Kipf | Google DeepMindtkipf.github.io
• Home | Petar Veličkovićpetar-v.com
• Beyond Message Passing: a Physics-Inspired Paradigm for Graph Neural Networksthegradient.pub
• geometricdeeplearning.com/book/graphs.htmlgeometricdeeplearning.com
• Distill — Latest articles about machine learningdistill.pub
• Aman's AI Journal • Primers • Ilya Sutskever's Top 30aman.ai
• [2207.02505] Pure Transformers are Powerful Graph Learnersarxiv.org
• Feature Visualizationdistill.pub
• Circuit Tracing: Revealing Computational Graphs in Language Modelstransformer-circuits.pub
• Convolutional Neural Networks, Explained | Towards Data Sciencetowardsdatascience.com
• [1801.07606] Deeper Insights into Graph Convolutional Networks for Semi-Supervised Learningarxiv.org