torch_geometric.nn.conv.SignedConv
- class SignedConv(in_channels: int, out_channels: int, first_aggr: bool, bias: bool = True, **kwargs)[source]
Bases:
MessagePassing来自“Signed Graph Convolutional Network”论文的签名图卷积操作符。
\[ \begin{align}\begin{aligned}\mathbf{x}_v^{(\textrm{pos})} &= \mathbf{\Theta}^{(\textrm{pos})} \left[ \frac{1}{|\mathcal{N}^{+}(v)|} \sum_{w \in \mathcal{N}^{+}(v)} \mathbf{x}_w , \mathbf{x}_v \right]\\\mathbf{x}_v^{(\textrm{neg})} &= \mathbf{\Theta}^{(\textrm{neg})} \left[ \frac{1}{|\mathcal{N}^{-}(v)|} \sum_{w \in \mathcal{N}^{-}(v)} \mathbf{x}_w , \mathbf{x}_v \right]\end{aligned}\end{align} \]如果
first_aggr设置为True，并且\[ \begin{align}\begin{aligned}\mathbf{x}_v^{(\textrm{pos})} &= \mathbf{\Theta}^{(\textrm{pos})} \left[ \frac{1}{|\mathcal{N}^{+}(v)|} \sum_{w \in \mathcal{N}^{+}(v)} \mathbf{x}_w^{(\textrm{pos})}, \frac{1}{|\mathcal{N}^{-}(v)|} \sum_{w \in \mathcal{N}^{-}(v)} \mathbf{x}_w^{(\textrm{neg})}, \mathbf{x}_v^{(\textrm{pos})} \right]\\\mathbf{x}_v^{(\textrm{neg})} &= \mathbf{\Theta}^{(\textrm{pos})} \left[ \frac{1}{|\mathcal{N}^{+}(v)|} \sum_{w \in \mathcal{N}^{+}(v)} \mathbf{x}_w^{(\textrm{neg})}, \frac{1}{|\mathcal{N}^{-}(v)|} \sum_{w \in \mathcal{N}^{-}(v)} \mathbf{x}_w^{(\textrm{pos})}, \mathbf{x}_v^{(\textrm{neg})} \right]\end{aligned}\end{align} \]否则。 如果
first_aggr是False，则该层期望x是一个张量，其中x[:, :in_channels]表示正节点特征 \(\mathbf{X}^{(\textrm{pos})}\)，而x[:, in_channels:]表示 负节点特征 \(\mathbf{X}^{(\textrm{neg})}\)。- Parameters:
in_channels (int) – Size of each input sample, or
-1to derive the size from the first input(s) to the forward method.out_channels (int) – Size of each output sample.
first_aggr (bool) – 表示使用哪种聚合公式。
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:
输入: 节点特征 \((|\mathcal{V}|, F_{in})\) 或 \(((|\mathcal{V_s}|, F_{in}), (|\mathcal{V_t}|, F_{in}))\) 如果是二分图， 正边索引 \((2, |\mathcal{E}^{(+)}|)\), 负边索引 \((2, |\mathcal{E}^{(-)}|)\)
outputs: node features \((|\mathcal{V}|, F_{out})\) or \((|\mathcal{V_t}|, F_{out})\) if bipartite