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mindspore_gl.nn.SAGEConv

class mindspore_gl.nn.SAGEConv(in_feat_size: int, out_feat_size: int, aggregator_type: str = 'pool', bias=True, norm=None, activation=None)

GraphSAGE Layer. From the paper Inductive Representation Learning on Large Graphs .

$$\begin{array}{r}\begin{array}{c}\begin{array}{c}{h}_{\mathcal{N}(i)}^{(l+1)}=\mathrm{aggregate}(\{h_j^l,\mathrm{\forall }j\in \mathcal{N}(i)\})\end{array}\\ \begin{array}{c}{h}_i^{(l+1)}=\sigma (W\cdot \mathrm{concat}(h_i^l,h_{\mathcal{N}(i)}^{l+1}))\end{array}\\ h_i^{(l+1)}=\mathrm{norm}(h_i^l)\end{array}\end{array}$$

If weights are provided on each edge, the weighted graph convolution is defined as:

$$h_{\mathcal{N}(i)}^{(l+1)}=\mathrm{aggregate}(\{e_{ji}{h}_j^l,\mathrm{\forall }j\in \mathcal{N}(i)\})$$

Parameters

• in_feat_size (int) – Input node feature size.

• out_feat_size (int) – Output node feature size.

• aggregator_type (str, optional) – Type of aggregator, should in 'pool', 'lstm' and 'mean'. Default: 'pool'.

• bias (bool, optional) – Whether use bias. Default: True.

• norm (Cell, optional) – Normalization function Cell. Default: None.

• activation (Cell, optional) – Activation function Cell. Default: None.

Inputs:

• x (Tensor) - The input node features. The shape is $(N,D\mathrm{\_}in)$ where $N$ is the number of nodes and $D\mathrm{\_}in$ could be of any shape.

• edge_weight (Tensor) - Edge weights. The shape is $(N\mathrm{\_}e,)$ where $N\mathrm{\_}e$ is the number of edges.

• g (Graph) - The input graph.

Outputs:

• Tensor, the output feature of shape $(N,D\mathrm{\_}out)$. where $N$ is the number of nodes and $D\mathrm{\_}out$ could be of any shape.

Raises

• TypeError – If in_feat_size or out_feat_size is not an int.

• TypeError – If bias is not a bool.

• KeyError – if aggregator type is not 'pool', 'lstm' or 'mean'.

• TypeError – if activation type is not mindspore.nn.Cell.

• TypeError – if norm type is not mindspore.nn.Cell.

Supported Platforms:

Ascend GPU

Examples

>>> import mindspore as ms
>>> from mindspore import nn
>>> from mindspore.numpy import ones
>>> from mindspore_gl.nn import SAGEConv
>>> from mindspore_gl import GraphField
>>> n_nodes = 4
>>> n_edges = 7
>>> feat_size = 4
>>> src_idx = ms.Tensor([0, 1, 1, 2, 2, 3, 3], ms.int32)
>>> dst_idx = ms.Tensor([0, 0, 2, 1, 3, 0, 1], ms.int32)
>>> ones = ms.ops.Ones()
>>> feat = ones((n_nodes, feat_size), ms.float32)
>>> graph_field = GraphField(src_idx, dst_idx, n_nodes, n_edges)
>>> sageconv = SAGEConv(in_feat_size=4, out_feat_size=2, activation=nn.ReLU())
>>> edge_weight = ones((n_edges, 1), ms.float32)
>>> res = sageconv(feat, edge_weight, *graph_field.get_graph())
>>> print(res.shape)
 (4,2)