Theory of graph neural networks: representation and learning
Stefanie Jegelka
Department of EECS, MIT, Cambridge, USA
![Theory of graph neural networks: representation and learning cover](/_next/image?url=https%3A%2F%2Fcontent.preview.ems.press%2Fassets%2Fpublic%2Fimages%2Fbooks%2Fcover-279.png&w=3840&q=90)
Download Chapter PDF
This book chapter is published open access.
Abstract
Graph Neural Networks (GNNs), neural network architectures targeted to learning representations of graphs, have become a popular learning model for prediction tasks on nodes, graphs and configurations of points, with wide success in practice. This article summarizes a selection of emerging theoretical results on approximation and learning properties of widely used message passing GNNs and higher-order GNNs, focusing on representation, generalization, and extrapolation. Along the way, it summarizes broad mathematical connections.