class SAGEConv(in_channels: Union[int, Tuple[int, int]], out_channels: int, aggr: Optional[Union[str, List[str], Aggregation]] = 'mean', normalize: bool = False, root_weight: bool = True, project: bool = False, bias: bool = True, **kwargs)[source]

Bases: MessagePassing

The GraphSAGE operator from the “Inductive Representation Learning on Large Graphs” paper.

\[\mathbf{x}^{\prime}_i = \mathbf{W}_1 \mathbf{x}_i + \mathbf{W}_2 \cdot \mathrm{mean}_{j \in \mathcal{N(i)}} \mathbf{x}_j\]

If project = True, then \(\mathbf{x}_j\) will first get projected via

\[\mathbf{x}_j \leftarrow \sigma ( \mathbf{W}_3 \mathbf{x}_j + \mathbf{b})\]

as described in Eq. (3) of the paper.

  • 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 or Aggregation, optional) – The aggregation scheme to use. Any aggregation of torch_geometric.nn.aggr can be used, e.g., "mean", "max", or "lstm". (default: "mean")

  • normalize (bool, optional) – If set to True, output features will be \(\ell_2\)-normalized, i.e., \(\frac{\mathbf{x}^{\prime}_i} {\| \mathbf{x}^{\prime}_i \|_2}\). (default: False)

  • root_weight (bool, optional) – If set to False, the layer will not add transformed root node features to the output. (default: True)

  • project (bool, optional) – If set to True, the layer will apply a linear transformation followed by an activation function before aggregation (as described in Eq. (3) of the paper). (default: False)

  • 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.

  • inputs: node features \((|\mathcal{V}|, F_{in})\) or \(((|\mathcal{V_s}|, F_{s}), (|\mathcal{V_t}|, F_{t}))\) if bipartite, edge indices \((2, |\mathcal{E}|)\)

  • outputs: node features \((|\mathcal{V}|, F_{out})\) or \((|\mathcal{V_t}|, F_{out})\) if bipartite

forward(x: Union[Tensor, Tuple[Tensor, Optional[Tensor]]], edge_index: Union[Tensor, SparseTensor], size: Optional[Tuple[int, int]] = None) Tensor[source]

Runs the forward pass of the module.


Resets all learnable parameters of the module.