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Showing content from https://pytorch-geometric.readthedocs.io/en/latest/generated/torch_geometric.nn.conv.ARMAConv.html below:

torch_geometric.nn.conv.ARMAConv — pytorch_geometric documentation

class ARMAConv(in_channels: int, out_channels: int, num_stacks: int = 1, num_layers: int = 1, shared_weights: bool = False, act: Optional[Callable] = ReLU(), dropout: float = 0.0, bias: bool = True, **kwargs)[source]

Bases: MessagePassing

The ARMA graph convolutional operator from the “Graph Neural Networks with Convolutional ARMA Filters” paper.

\[\mathbf{X}^{\prime} = \frac{1}{K} \sum_{k=1}^K \mathbf{X}_k^{(T)},\]

with \(\mathbf{X}_k^{(T)}\) being recursively defined by

\[\mathbf{X}_k^{(t+1)} = \sigma \left( \mathbf{\hat{L}} \mathbf{X}_k^{(t)} \mathbf{W} + \mathbf{X}^{(0)} \mathbf{V} \right),\]

where \(\mathbf{\hat{L}} = \mathbf{I} - \mathbf{L} = \mathbf{D}^{-1/2} \mathbf{A} \mathbf{D}^{-1/2}\) denotes the modified Laplacian \(\mathbf{L} = \mathbf{I} - \mathbf{D}^{-1/2} \mathbf{A} \mathbf{D}^{-1/2}\).

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 \(\mathbf{x}^{(t+1)}\).

• num_stacks (int, optional) – Number of parallel stacks \(K\). (default: 1).

• num_layers (int, optional) – Number of layers \(T\). (default: 1)

• act (callable, optional) – Activation function \(\sigma\). (default: torch.nn.ReLU())

• shared_weights (int, optional) – If set to True the layers in each stack will share the same parameters. (default: False)

• dropout (float, optional) – Dropout probability of the skip connection. (default: 0.)

• 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 \((2, |\mathcal{E}|)\), 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.

Return type:

Tensor

reset_parameters()[source]

Resets all learnable parameters of the module.

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