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:
MessagePassingThe dynamic neighborhood aggregation operator from the “Just Jump: Towards Dynamic Neighborhood Aggregation in Graph Neural Networks” paper.
\[\mathbf{x}_v^{(t)} = h_{\mathbf{\Theta}}^{(t)} \left( \mathbf{x}_{v \leftarrow v}^{(t)}, \left\{ \mathbf{x}_{v \leftarrow w}^{(t)} : w \in \mathcal{N}(v) \right\} \right)\]based on (multi-head) dot-product attention
\[\mathbf{x}_{v \leftarrow w}^{(t)} = \textrm{Attention} \left( \mathbf{x}^{(t-1)}_v \, \mathbf{\Theta}_Q^{(t)}, [\mathbf{x}_w^{(1)}, \ldots, \mathbf{x}_w^{(t-1)}] \, \mathbf{\Theta}_K^{(t)}, \, [\mathbf{x}_w^{(1)}, \ldots, \mathbf{x}_w^{(t-1)}] \, \mathbf{\Theta}_V^{(t)} \right)\]with \(\mathbf{\Theta}_Q^{(t)}, \mathbf{\Theta}_K^{(t)}, \mathbf{\Theta}_V^{(t)}\) denoting (grouped) projection matrices for query, key and value information, respectively. \(h^{(t)}_{\mathbf{\Theta}}\) is implemented as a non-trainable version of
torch_geometric.nn.conv.GCNConv.Note
In contrast to other layers, this operator expects node features as shape
[num_nodes, num_layers, channels].- Parameters:
channels (int) – Size of each input/output sample.
heads (int, optional) – Number of multi-head-attentions. (default:
1)groups (int, optional) – Number of groups to use for all linear projections. (default:
1)dropout (float, optional) – Dropout probability of attention coefficients. (default:
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:
input: node features \((|\mathcal{V}|, L, F)\) where \(L\) is the number of layers, edge indices \((2, |\mathcal{E}|)\)
output: node features \((|\mathcal{V}|, F)\)
- forward(x: Tensor, edge_index: Union[Tensor, SparseTensor], edge_weight: Optional[Tensor] = None) Tensor[source]
Runs the forward pass of the module.
- Parameters:
x (torch.Tensor) – The input node features of shape
[num_nodes, num_layers, channels].edge_index (torch.Tensor or SparseTensor) – The edge indices.
edge_weight (torch.Tensor, optional) – The edge weights. (default:
None)