gammagl.layers.conv.SGConv

class SGConv(in_channels, out_channels, iter_K=2)

The simple graph convolutional operator from the “Simplifying Graph Convolutional Networks” paper

\[\mathbf{X}^{\prime} = {\left(\mathbf{\hat{D}}^{-1/2} \mathbf{\hat{A}} \mathbf{\hat{D}}^{-1/2} \right)}^K \mathbf{X} \mathbf{\Theta},\]

where \(\mathbf{\hat{A}} = \mathbf{A} + \mathbf{I}\) denotes the adjacency matrix with inserted self-loops and \(\hat{D}_{ii} = \sum_{j=0} \hat{A}_{ij}\) its diagonal degree matrix. The adjacency matrix can include other values than 1 representing edge weights via the optional edge_weight tensor.

Parameters:

• in_channels (int) – Size of each input sample, or -1 to derive the size from the first input(s) to the forward method.

• out_channels (int) – Size of each output sample.

• iter_K (int, optional) – Number of hops \(K\). (default: 1)

forward(x, edge_index, edge_weight=None, num_nodes=None)