Simple Graph Convolutional Networks
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.
ArXiv