The Graph Convolutional Network (GCN) model, introduced by Kipf and Welling, achieves powerful graph representations by stacking simple convolutional layers and utilizing a normalized variant of the adjacency matrix with self-loops. The layer-wise propagation rule is defined as:

$\mathbf{H}^{(k)}=\sigma(\tilde{\mathbf{A}} \mathbf{H}^{(k-1)} \mathbf{W}^{(k)})$

where $\tilde{\mathbf{A}} = (\mathbf{D}+\mathbf{I})^{-\frac{1}{2}} (\mathbf{I}+\mathbf{A}) (\mathbf{D}+\mathbf{I})^{-\frac{1}{2}}$.