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mindspore_gl.nn

APIs for graph convolutions.

class mindspore_gl.nn.APPNPConv(k: int, alpha: float, edge_drop=1.0)

Approximate Personalization Propagation in Neural Prediction Layers. From the paper Predict then Propagate: Graph Neural Networks meet Personalized PageRank.

$$\begin{array}{r}{H}^0=X\\ H^{l+1}=(1-\alpha )\left({\tilde{D}}^{-1/2}\tilde{A}{\tilde{D}}^{-1/2}{H}^l\right)+\alpha H^0\end{array}$$

Where $\tilde{A}=A+I$

Parameters

• k (int) – Number of iters.

• alpha (float) – Transmission probability.

• edge_drop (float) – The drop rate on the edge of messages received by each node. Default: 1.0.

Inputs:

• x (Tensor): The input node features. The shape is $(N,\ast )$ where $N$ is the number of nodes, and $\ast$ could be of any shape.

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

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

• g (Graph): The input graph.

Outputs:

Tensor, the output feature of shape $(N,\ast )$ where $\ast$ should be the same as input shape.

Raises

• TypeError – If k is not an int.

• TypeError – If alpha or edge_drop is not a float.

• ValueError – If alpha is not in range [0.0, 1.0]

• ValueError – If edge_drop is not in range (0.0, 1.0]

Examples

>>> import mindspore as ms
>>> from mindspore_gl.nn.conv import APPNPConv
>>> 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)
>>> appnpconv = APPNPConv(k=3, alpha=0.5, edge_drop=1.0)
>>> res = appnpconv(feat, in_degree, out_degree, *graph_field.get_graph())
>>> print(res.shape)
(4, 4)

class mindspore_gl.nn.GATConv(in_feat_size: int, out_size: int, num_attn_head: int, input_drop_out_rate: float = 1.0, attn_drop_out_rate: float = 1.0, leaky_relu_slope: float = 0.2, activation=None, add_norm=False)

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 $j$.

$$\begin{array}{r}{\alpha }_{ij}^l={\mathrm{softmax}}_{\mathrm{i}}(e_{ij}^l)\\ e_{ij}^l=\mathrm{LeakyReLU}({\overrightarrow{a}}^T[W{h}_i\Vert W{h}_j])\end{array}$$

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) – Input drop out rate. Default: 1.0.

• attn_drop_out_rate (float) – Attention drop out rate. Default: 1.0.

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

• activation (Cell) – Activation function, default is None.

• add_norm – 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 $D_{in}\ast num\mathrm{\_}attn\mathrm{\_}head$.

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 Cell.

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

Examples

>>> import mindspore as ms
>>> from mindspore_gl.nn.conv 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)

class mindspore_gl.nn.GCNConv(in_feat_size: int, out_size: int, activation=None, dropout=0.5)

Graph Convolution Network Layer. from the paper Semi-Supervised Classification with Graph Convolutional Networks.

$$h_i^{(l+1)}=\sigma (b^{(l)}+\sum _{j\in \mathcal{N}(i)}\frac{1}{c_{ji}}{h}_j^{(l)}{W}^{(l)})$$

$\mathcal{N}(i)$ represents the neighbour node of $i$. $c_{ji}=\sqrt{|\mathcal{N}(j)|}\sqrt{|\mathcal{N}(i)|}$.

$$h_i^{(l+1)}=\sigma (b^{(l)}+\sum _{j\in \mathcal{N}(i)}\frac{e_{ji}}{c_{ji}}{h}_j^{(l)}{W}^{(l)})$$

Parameters

• in_feat_size (int) – Input node feature size.

• out_size (int) – Output node feature size.

• activation (Cell) – Activation function, default is None.

• dropout (float) – The keep rate, greater than 0 and less equal than 1. E.g. dropout=0.9, dropping out 10% of input units. Default: 0.5.

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_size in Args.

Raises

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

• TypeError – If dropout is not a float.

• TypeError – If activation is not a Cell.

• ValueError – If dropout is not in range (0.0, 1.0]

Supported Platforms:

GPU

Examples

>>> import mindspore as ms
>>> from mindspore_gl.nn.conv import GCNConv
>>> 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)
>>> gcnconv = GCNConv(in_feat_size=4, out_size=2, activation=None, dropout=1.0)
>>> res = gcnconv(feat, in_degree, out_degree, *graph_field.get_graph())
>>> print(res.shape)
(4, 2)

class mindspore_gl.nn.GNNCell

GNN Cell class.

Construct function will be translated by default.

static disable_display()

Disable display code comparison.

static enable_display(screen_width=200)

Enable display code comparison.

Parameters

screen_width (int) – Determines the screen width on which the code is displayed.