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mindspore_gl.nn.APPNPConv

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 keep 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]

Supported Platforms:

Ascend GPU

Examples

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