mindspore_gl.nn.APPNPConv

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

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

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

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 \((N,*)\) where \(N\) is the number of nodes, and \(*\) 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 \((N,*)\) where \(*\) 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)