torch_geometric.nn.conv.TAGConv

class TAGConv(in_channels: int, out_channels: int, K: int = 3, bias: bool = True, normalize: bool = True, **kwargs)[source]

Bases: MessagePassing

The topology adaptive graph convolutional networks operator from the “Topology Adaptive Graph Convolutional Networks” paper.

\[\mathbf{X}^{\prime} = \sum_{k=0}^K \left( \mathbf{D}^{-1/2} \mathbf{A} \mathbf{D}^{-1/2} \right)^k \mathbf{X} \mathbf{W}_{k},\]

where \(\mathbf{A}\) denotes the adjacency matrix and \(D_{ii} = \sum_{j=0} A_{ij}\) 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.

  • K (int, optional) – Number of hops \(K\). (default: 3)

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

  • normalize (bool, optional) – Whether to apply symmetric normalization. (default: True)

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

Shapes:

  • input: node_features \((|\mathcal{V}|, F_{in})\), edge_index \((2, |\mathcal{E}|)\), edge_weights \((|\mathcal{E}|)\) (optional)

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

forward(x: Tensor, edge_index: Union[Tensor, SparseTensor], edge_weight: Optional[Tensor] = None) → Tensor[source]

Runs the forward pass of the module.

reset_parameters()[source]

Resets all learnable parameters of the module.