torch_geometric.nn.conv.GraphConv
- class GraphConv(in_channels: Union[int, Tuple[int, int]], out_channels: int, aggr: str = 'add', bias: bool = True, **kwargs)[source]
Bases:
MessagePassing
The graph neural network operator from the “Weisfeiler and Leman Go Neural: Higher-order Graph Neural Networks” paper.
\[\mathbf{x}^{\prime}_i = \mathbf{W}_1 \mathbf{x}_i + \mathbf{W}_2 \sum_{j \in \mathcal{N}(i)} e_{j,i} \cdot \mathbf{x}_j\]where \(e_{j,i}\) denotes the edge weight from source node
j
to target nodei
(default:1
)- Parameters:
in_channels (int or tuple) – Size of each input sample, or
-1
to derive the size from the first input(s) to the forward method. A tuple corresponds to the sizes of source and target dimensionalities.out_channels (int) – Size of each output sample.
aggr (str, optional) – The aggregation scheme to use (
"add"
,"mean"
,"max"
). (default:"add"
)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 \((|\mathcal{V}|, F_{in})\) or \(((|\mathcal{V_s}|, F_{s}), (|\mathcal{V_t}|, F_{t}))\) if bipartite, edge indices \((2, |\mathcal{E}|)\), edge weights \((|\mathcal{E}|)\) (optional)
output: node features \((|\mathcal{V}|, F_{out})\) or \((|\mathcal{V}_t|, F_{out})\) if bipartite