# torch_geometric.nn.conv.SAGEConv

class SAGEConv(in_channels: Union[int, Tuple[int, int]], out_channels: int, aggr: = '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.

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 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.

Shapes:
• 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: , edge_index: Union[Tensor, SparseTensor], size: Optional[Tuple[int, int]] = None) [source]

Runs the forward pass of the module.

reset_parameters()[source]

Resets all learnable parameters of the module.