torch_geometric.nn.conv.DNAConv
- class DNAConv(channels: int, heads: int = 1, groups: int = 1, dropout: float = 0.0, cached: bool = False, normalize: bool = True, add_self_loops: bool = True, bias: bool = True, **kwargs)[source]
Bases:
MessagePassing来自“Just Jump: Towards Dynamic Neighborhood Aggregation in Graph Neural Networks”论文的动态邻域聚合操作符。
\[\mathbf{x}_v^{(t)} = h_{\mathbf{\Theta}}^{(t)} \left( \mathbf{x}_{v \leftarrow v}^{(t)}, \left\{ \mathbf{x}_{v \leftarrow w}^{(t)} : w \in \mathcal{N}(v) \right\} \right)\]基于（多头）点积注意力
\[\mathbf{x}_{v \leftarrow w}^{(t)} = \textrm{Attention} \left( \mathbf{x}^{(t-1)}_v \, \mathbf{\Theta}_Q^{(t)}, [\mathbf{x}_w^{(1)}, \ldots, \mathbf{x}_w^{(t-1)}] \, \mathbf{\Theta}_K^{(t)}, \, [\mathbf{x}_w^{(1)}, \ldots, \mathbf{x}_w^{(t-1)}] \, \mathbf{\Theta}_V^{(t)} \right)\]使用 \(\mathbf{\Theta}_Q^{(t)}, \mathbf{\Theta}_K^{(t)}, \mathbf{\Theta}_V^{(t)}\) 分别表示查询、键和值信息的（分组）投影矩阵。 \(h^{(t)}_{\mathbf{\Theta}}\) 被实现为
torch_geometric.nn.conv.GCNConv的不可训练版本。注意
与其他层不同，此操作符期望节点特征的形状为
[num_nodes, num_layers, channels]。- Parameters:
channels (int) – 每个输入/输出样本的大小。
heads (int, optional) – Number of multi-head-attentions. (default:
1)groups (int, optional) – 用于所有线性投影的组数。(默认:
1)dropout (float, optional) – 注意力系数的丢弃概率。（默认值：
0.）cached (bool, optional) – If set to
True, the layer will cache the computation of \(\mathbf{\hat{D}}^{-1/2} \mathbf{\hat{A}} \mathbf{\hat{D}}^{-1/2}\) on first execution, and will use the cached version for further executions. This parameter should only be set toTruein transductive learning scenarios. (default:False)normalize (bool, optional) – Whether to add self-loops and apply symmetric normalization. (default:
True)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 \((|\mathcal{V}|, L, F)\) 其中 \(L\) 是 层数, 边索引 \((2, |\mathcal{E}|)\)
output: node features \((|\mathcal{V}|, F)\)
- forward(x: Tensor, edge_index: Union[Tensor, SparseTensor], edge_weight: Optional[Tensor] = None) Tensor[source]
运行模块的前向传播。
- Parameters:
x (torch.Tensor) – 输入节点特征的形状为
[num_nodes, num_layers, channels]。edge_index (torch.Tensor or SparseTensor) – The edge indices.
edge_weight (torch.Tensor, optional) – The edge weights. (default:
None)
- Return type: