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]

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

为每个可能的顶点度数训练一个独特的权重矩阵。

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) – 更新权重时考虑的最大节点度数（默认值：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]

运行模块的前向传播。