2017
Semi-Supervised Classification with Graph Convolutional Networks
ICLR 2017poster
We present a scalable approach for semi-supervised learning on graph-structured data that is based on an efficient variant of convolutional neural networks which operate directly on graphs. We motivate the choice of our convolutional architecture via a localized first-order approximation of spectral…