A graph convolutional network model is proposed for semi-supervised learning that takes into account both the graph structure and node features. The model uses a graph convolutional layer that approximates spectral graph convolutions using a localized first-order approximation. This allows the model to be applied to large-scale problems. The model is evaluated on several benchmark semi-supervised classification datasets where it achieves state-of-the-art performance.