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:
MessagePassingThe 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
-1to 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