mindspore_gl.nn.GATConv

class mindspore_gl.nn.GATConv(in_feat_size: int, out_size: int, num_attn_head: int, input_drop_out_rate: float = 0.0, attn_drop_out_rate: float = 0.0, leaky_relu_slope: float = 0.2, activation=None, add_norm=False)[source]

Graph Attention Network. From the paper Graph Attention Network .

\[h_i^{(l+1)} = \sum_{j\in \mathcal{N}(i)} \alpha_{i,j} W^{(l)} h_j^{(l)}\]

\(\alpha_{i, j}\) represents the attention score between node \(i\) and node \(j\).

\[\begin{split}\alpha_{ij}^{l} = \mathrm{softmax_i} (e_{ij}^{l}) \\ e_{ij}^{l} = \mathrm{LeakyReLU}\left(\vec{a}^T [W h_{i} \| W h_{j}]\right)\end{split}\]
Parameters

  • in_feat_size (int) – Input node feature size.

  • out_size (int) – Output node feature size.

  • num_attn_head (int) – Number of attention head used in GAT.

  • input_drop_out_rate (float, optional) – Dropout rate of input drop out. Default: 0.0.

  • attn_drop_out_rate (float, optional) – Dropout rate of attention drop out. Default: 0.0.

  • leaky_relu_slope (float, optional) – Slope for leaky relu. Default: 0.2.

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

  • add_norm (bool, optional) – Whether the edge information needs normalization or not. Default: False.

Inputs:

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

  • g (Graph) - The input graph.

Outputs:

  • Tensor, the output feature of shape \((N,D_{out})\) where \(D_{out}\) should be equal to \(D_{in} * num\_attn\_head\).

Raises

  • TypeError – If in_feat_size, out_size, or num_attn_head is not an int.

  • TypeError – If input_drop_out_rate, attn_drop_out_rate, or leaky_relu_slope is not a float.

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

  • ValueError – If input_drop_out_rate or attn_drop_out_rate is not in range (0.0, 1.0]

Supported Platforms:

Ascend GPU

Examples

>>> import mindspore as ms
>>> from mindspore_gl.nn import GATConv
>>> 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)
>>> gatconv = GATConv(in_feat_size=4, out_size=2, num_attn_head=3)
>>> res = gatconv(feat, *graph_field.get_graph())
>>> print(res.shape)
(4, 6)