torch_geometric.nn.conv.PDNConv
- class PDNConv(in_channels: int, out_channels: int, edge_dim: int, hidden_channels: int, add_self_loops: bool = True, normalize: bool = True, bias: bool = True, **kwargs)[source]
Bases:
MessagePassing来自“Pathfinder Discovery Networks for Neural Message Passing”论文的路径发现网络卷积算子。
\[\mathbf{x}^{\prime}_i = \sum_{j \in \mathcal{N}(i) \cup \{i\}}f_{\Theta}(\textbf{e}_{(j,i)}) \cdot f_{\Omega}(\mathbf{x}_{j})\]其中 \(z_{i,j}\) 表示从源节点 \(j\) 到目标节点 \(i\) 的边特征向量，\(\mathbf{x}_{j}\) 表示节点 \(j\) 的节点特征向量。
- Parameters:
in_channels (int) – Size of each input sample.
out_channels (int) – Size of each output sample.
edge_dim (int) – Edge feature dimensionality.
hidden_channels (int) – 隐藏的边缘特征维度。
add_self_loops (bool, optional) – If set to
False, will not add self-loops to the input graph. (default:True)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 \((|\mathcal{V}|, F_{in})\), 边索引 \((2, |\mathcal{E}|)\), 边特征 \((|\mathcal{E}|, D)\) (可选)
output: node features \((|\mathcal{V}|, F_{out})\)