torch_geometric.nn.conv.ClusterGCNConv

class ClusterGCNConv(in_channels: int, out_channels: int, diag_lambda: float = 0.0, add_self_loops: bool = True, bias: bool = True, **kwargs)[source]

Bases: MessagePassing

The ClusterGCN graph convolutional operator from the “Cluster-GCN: An Efficient Algorithm for Training Deep and Large Graph Convolutional Networks” paper.

\[\mathbf{X}^{\prime} = \left( \mathbf{\hat{A}} + \lambda \cdot \textrm{diag}(\mathbf{\hat{A}}) \right) \mathbf{X} \mathbf{W}_1 + \mathbf{X} \mathbf{W}_2\]

where \(\mathbf{\hat{A}} = {(\mathbf{D} + \mathbf{I})}^{-1}(\mathbf{A} + \mathbf{I})\).

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.

  • diag_lambda (float, optional) – Diagonal enhancement value \(\lambda\). (default: 0.)

  • add_self_loops (bool, optional) – If set to False, will not add self-loops to the input graph. (default: True)

  • bias (bool, optional) – If set to False, the layer will not learn an additive bias. (default: True)

  • **kwargs (optional) – Additional arguments of torch_geometric.nn.conv.MessagePassing.

Shapes:

  • input: node features \((|\mathcal{V}|, F_{in})\), edge indices \((2, |\mathcal{E}|)\)

  • output: node features \((|\mathcal{V}|, F_{out})\)

forward(x: Tensor, edge_index: Union[Tensor, SparseTensor]) → Tensor[source]

Runs the forward pass of the module.

reset_parameters()[source]

Resets all learnable parameters of the module.