Semi-Supervised Classification with Graph Convolutional Networks
Proceedings of the 5th International Conference on Learning Representations, (2017)Abstract
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 graph convolutions.
Our model scales linearly in the number of graph edges and learns hidden layer
representations that encode both local graph structure and features of nodes. In
a number of experiments on citation networks and on a knowledge graph dataset
we demonstrate that our approach outperforms related methods by a significant
margin.
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