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.