mindspore_gl.nn.TAGConv

class mindspore_gl.nn.TAGConv(in_feat_size: int, out_feat_size: int, num_hops: int = 2, bias: bool = True, activation=None)[source]

Topology adaptation graph convolutional layer. From the paper Topology Adaptive Graph Convolutional Networks .

\[H^{K} = {\sum}_{k=0}^K (D^{-1/2} A D^{-1/2})^{k} X {\Theta}_{k}\]

where \({\Theta}_{k}\) represents a linear weight to add the results of different hop counts.

Parameters

  • in_feat_size (int) – Input node feature size.

  • out_feat_size (int) – Output node feature size.

  • num_hops (int, optional) – Number of hops. Default: 2.

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

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

Inputs:

  • x (Tensor) - The input node features. The shape is \((N, D_{in})\) where \(N\) is the number of nodes, and \(D_{in}\) should be equal to in_feat_size in Args.

  • in_deg (Tensor) - In degree for nodes. The shape is \((N, )\) where \(N\) is the number of nodes.

  • out_deg (Tensor) - Out degree for nodes. The shape is \((N, )\) where \(N\) is the number of nodes.

  • g (Graph) - The input graph.

Outputs:

  • Tensor, output node features with shape of \((N, D_{out})\), where \((D_{out})\) should be the same as out_feat_size in Args.

Raises

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

  • TypeError – If bias is not a bool.

  • TypeError – If activation is not a mindspore.nn.Cell.

Supported Platforms:

Ascend GPU

Examples

>>> import mindspore as ms
>>> from mindspore_gl.nn import TAGConv
>>> 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)
>>> in_degree = ms.Tensor([3, 2, 1, 1], ms.int32)
>>> out_degree = ms.Tensor([1, 2, 1, 2], ms.int32)
>>> tagconv = TAGConv(in_feat_size=4, out_feat_size=2, activation=None, num_hops=3)
>>> res = tagconv(feat, in_degree, out_degree, *graph_field.get_graph())
>>> print(res.shape)
(4, 2)