Related papers: Simple Graph Convolutional Networks

Simple Graph Convolutional Networks

URL: http://arxiv.org/abs/2106.05809v1
Date: Thu, 10 Jun 2021 15:23:59 GMT
Title: Simple Graph Convolutional Networks
Authors: Luca Pasa, Nicol\`o Navarin, Wolfgang Erb, Alessandro Sperduti
Abstract summary: We propose simple graph convolution operators, that can be implemented in single-layer graph convolutional networks. We show that our convolution operators are more theoretically grounded than many proposals in literature, and exhibit state-of-the-art predictive performance on the considered benchmark datasets.
Score: 72.92604941595019
License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
Abstract: Many neural networks for graphs are based on the graph convolution operator, proposed more than a decade ago. Since then, many alternative definitions have been proposed, that tend to add complexity (and non-linearity) to the model. In this paper, we follow the opposite direction by proposing simple graph convolution operators, that can be implemented in single-layer graph convolutional networks. We show that our convolution operators are more theoretically grounded than many proposals in literature, and exhibit state-of-the-art predictive performance on the considered benchmark datasets.

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