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arXiv · 2204.07697

Theory of Graph Neural Networks: Representation and Learning

Stefanie Jegelka
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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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Stefanie Jegelka. 2022-04-16. Theory of Graph Neural Networks: Representation and Learning. https://arxiv.org/abs/2204.07697

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