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Radim Špetlík
Semi-supervised classification with graph convolutional networks
On 2018-11-20 13:30 at G205
Reading group on the work by T. Kipf and M. Welling published in ICLR 2017. 
Presented by Radim Špetlík.

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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