Simple Graph Convolutional Networks
This work addresses the problem of overcomplicated graph convolution methods for researchers and practitioners in graph machine learning, though it is incremental in simplifying existing approaches.
The paper tackles the complexity in graph neural networks by proposing simple graph convolution operators for single-layer networks, achieving state-of-the-art predictive performance on benchmark datasets.
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.