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mindspore_gl.nn.EGConv

class mindspore_gl.nn.EGConv(in_feat_size: int, out_feat_size: int, aggregators: List[str], num_heads: int = 8, num_bases: int = 4, bias: bool = True)[source]

Efficient Graph Convolution. From the paper Adaptive Filters and Aggregator Fusion for Efficient Graph Convolutions .

h_{i}^{(l + 1)} = {| |}_{h = 1}^{H} \sum_{\oplus \in A} \sum_{b = 1}^{B} w_{h, \oplus, b}^{(l)} ⨁_{j \in N (i)} W_{b}^{(l)} h_{j}^{(l)}

$N (i)$ represents the neighbour node of $i$ , $W_{b}^{(l)}$ represents a basis weight, $\oplus$ represents an aggregator, $w_{h, \oplus, b}^{(l)}$ represents per-vertex weighting coefficients across heads, aggregator and bases.

Parameters

in_feat_size (int) – Input node feature size.
out_feat_size (int) – Output node feature size.
aggregators (str, optional) – aggregators to be used. Supported aggregators are 'sum', 'mean', 'max', 'min', 'std', 'var', 'symnorm'.
num_heads (int, optional) – Number of heads $H$ . Default: 8. Must have $o u t_f e a t_s i z e$ .
num_bases (int, optional) – Number of basis weight $B$ . Default: 4.
bias (bool, optional) – Whether the layer will learn an additive bias. Default: True.

Inputs:

x (Tensor) - The input node features. The shape is $(N, D_{i n})$ where $N$ is the number of nodes, and $D_{i n}$ should be equal to in_feat_size in Args.
g (Graph) - The input graph.

Outputs:

Tensor, output node features with shape of $(N, D_{o u t})$ , where $(D_{o u t})$ should be the same as out_feat_size in Args.

Raises

TypeError – If in_feat_size or out_feat_size or num_heads is not a positive int.
ValueError – If out_feat_size is not divisible by ‘num_heads’.
ValueError – If aggregators is not 'sum', 'mean', 'max', 'min', 'symnorm', 'var' or 'std'.

Supported Platforms:: Ascend GPU

Examples

>>> import mindspore as ms
>>> from mindspore_gl.nn import EGConv
>>> 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)
>>> conv = EGConv(in_feat_size=4, out_feat_size=6, aggregators=['sum'], num_heads=3, num_bases=3)
>>> res = conv(feat, *graph_field.get_graph())
>>> print(res.shape)
(4, 6)