torch_geometric.nn.conv.MFConv

class MFConv(in_channels: Union[int, Tuple[int, int]], out_channels: int, max_degree: int = 10, bias=True, **kwargs)[source]

Bases: MessagePassing

The graph neural network operator from the “Convolutional Networks on Graphs for Learning Molecular Fingerprints” paper.

\[\mathbf{x}^{\prime}_i = \mathbf{W}^{(\deg(i))}_1 \mathbf{x}_i + \mathbf{W}^{(\deg(i))}_2 \sum_{j \in \mathcal{N}(i)} \mathbf{x}_j\]

which trains a distinct weight matrix for each possible vertex degree.

Parameters:

• in_channels (int or tuple) – Size of each input sample, or -1 to derive the size from the first input(s) to the forward method. A tuple corresponds to the sizes of source and target dimensionalities.

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

• max_degree (int, optional) – The maximum node degree to consider when updating weights (default: 10)

• 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:

• inputs: node features \((|\mathcal{V}|, F_{in})\) or \(((|\mathcal{V_s}|, F_{s}), (|\mathcal{V_t}|, F_{t}))\) if bipartite, edge indices \((2, |\mathcal{E}|)\)

• outputs: node features \((|\mathcal{V}|, F_{out})\) or \((|\mathcal{V_t}|, F_{out})\) if bipartite

forward(x: Union[Tensor, Tuple[Tensor, Optional[Tensor]]], edge_index: Union[Tensor, SparseTensor], size: Optional[Tuple[int, int]] = None) → Tensor[source]

Runs the forward pass of the module.

Return type:

Tensor

reset_parameters()[source]

Resets all learnable parameters of the module.