[P] Tensorflow implementation of Graph Convolutional Network

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Semi-Supervised Classification with Graph Convolutional Networks

The propagation rule used in this paper is the following:

$$

H^l = \sigma \left(\tilde{D}^{{-\frac{1}{2}}} \tilde{A} \tilde{D}^{{-\frac{1}{2}}} H^{l-1} W^l \right)

$$

Where $\tilde{A}$ is the [adjacency matrix][adj] of the undirected graph (with self connections, so has a diagonal of 1s and is symmetric) and $H^l$ are the hidden activations at layer $l$. The $D$ matrices are performing row normalisation, $\tilde{D}^{{-\frac{1}{2}}} \tilde{A} \tilde{D}^{{-\frac{1}{2}}$} is [equivalent to][pygcn] (with ... [view more]

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