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.

中文翻译：

---

简单图卷积网络

许多用于图的神经网络都基于十多年前提出的图卷积算子。从那时起，提出了许多替代定义，这些定义往往会增加模型的复杂性（和非线性）。在本文中，我们通过提出可以在单层图卷积网络中实现的简单图卷积算子来遵循相反的方向。我们表明，我们的卷积算子比文献中的许多建议更具理论基础，并在所考虑的基准数据集上表现出最先进的预测性能。