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
The pathfinder discovery network convolutional operator from the “Pathfinder Discovery Networks for Neural Message Passing” paper.
\[\mathbf{x}^{\prime}_i = \sum_{j \in \mathcal{N}(i) \cup \{i\}}f_{\Theta}(\textbf{e}_{(j,i)}) \cdot f_{\Omega}(\mathbf{x}_{j})\]where \(z_{i,j}\) denotes the edge feature vector from source node \(j\) to target node \(i\), and \(\mathbf{x}_{j}\) denotes the node feature vector of node \(j\).
- 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) – Hidden edge feature dimensionality.
add_self_loops (bool, optional) – If set to
False
, will not add self-loops to the input graph. (default:True
)normalize (bool, optional) – Whether to add self-loops and compute symmetric normalization coefficients on the fly. (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 \((|\mathcal{V}|, F_{in})\), edge indices \((2, |\mathcal{E}|)\), edge features \((|\mathcal{E}|, D)\) (optional)
output: node features \((|\mathcal{V}|, F_{out})\)