# torch_geometric.nn.conv.ARMAConv

class ARMAConv(in_channels: int, out_channels: int, num_stacks: int = 1, num_layers: int = 1, shared_weights: bool = False, act: = 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: = None) [source]

Runs the forward pass of the module.

reset_parameters()[source]

Resets all learnable parameters of the module.