Theory of Graph Neural Networks: Representation and Learning

• URL: http://arxiv.org/abs/2204.07697v1
• Date: Sat, 16 Apr 2022 02:08:50 GMT
• Title: Theory of Graph Neural Networks: Representation and Learning
• Authors: Stefanie Jegelka
• Abstract summary: Graph Neural Networks (GNNs) have become a popular learning model for prediction tasks on nodes, graphs and configurations of points. This article summarizes a selection of the emerging theoretical results on approximation and learning properties of widely used message passing GNNs and higher-order GNNs.
• Score: 44.02161831977037
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• 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 the 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 mathematical connections.

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