# Copyright 2022 Huawei Technologies Co., Ltd
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
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# ============================================================================
"""APPNPConv Layer."""
import mindspore as ms
from mindspore import Tensor
from mindspore_gl import Graph
from .. import GNNCell
[文档]class APPNPConv(GNNCell):
r"""
Approximate Personalization Propagation in Neural Prediction Layers.
From the paper `Predict then Propagate: Graph Neural Networks meet Personalized
PageRank <https://arxiv.org/pdf/1810.05997.pdf>`_ .
.. math::
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}
Where :math:`\tilde{A}=A+I`
Args:
k (int): Number of iters.
alpha (float): Transmission probability.
edge_drop (float, optional): The dropout rate on the edge of messages received by each node. Default: ``0.0``.
Inputs:
- **x** (Tensor): The input node features. The shape is :math:`(N,*)` where :math:`N` is the number of nodes,
and :math:`*` could be of any shape.
- **in_deg** (Tensor): In degree for nodes. In degree for nodes. The shape is :math:`(N, )` where :math:`N` is
the number of nodes.
- **out_deg** (Tensor): Out degree for nodes. Out degree for nodes. The shape is :math:`(N, )`
where :math:`N` is the number of nodes.
- **g** (Graph): The input graph.
Outputs:
- Tensor, the output feature of shape :math:`(N,*)` where :math:`*` 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)
"""
def __init__(self,
k: int,
alpha: float,
edge_drop=0.0):
super().__init__()
if k <= 0 or not isinstance(k, int):
raise ValueError("k must be positive int")
if not isinstance(alpha, float):
raise ValueError("alpha must be float")
if not isinstance(edge_drop, float):
raise ValueError("edge_drop must be float")
self.k_ = k
self.alpha_ = alpha
if self.alpha_ < 0.0 or self.alpha_ > 1.0:
raise ValueError(f"For '{self.cls_name}', the 'alpha' should be a number in range [0.0, 1.0], "
f"but got {self.alpha_}.")
if edge_drop < 0.0 or edge_drop >= 1.0:
raise ValueError(f"For '{self.cls_name}', the 'edge_drop' should be a number in range [0.0, 1.0), "
f"but got {edge_drop}.")
self.edge_drop = ms.nn.Dropout(p=edge_drop)
self.min_clip = Tensor(1, ms.int32)
self.max_clip = Tensor(10000000, ms.int32)
# pylint: disable=arguments-differ
def construct(self, x, in_deg, out_deg, g: Graph):
"""
Construct function for APPNPConv.
"""
out_deg = ms.ops.clip_by_value(out_deg, self.min_clip, self.max_clip)
out_deg = ms.ops.Reshape()(ms.ops.Pow()(out_deg, -0.5), ms.ops.Shape()(out_deg) + (1,))
in_deg = ms.ops.clip_by_value(in_deg, self.min_clip, self.max_clip)
in_deg = ms.ops.Reshape()(ms.ops.Pow()(in_deg, -0.5), ms.ops.Shape()(in_deg) + (1,))
feat0 = x
g.set_vertex_attr({'x': x, 'in_deg': in_deg, 'out_deg': out_deg})
for _ in range(self.k_):
for v in g.dst_vertex:
v.h = g.sum(self.edge_drop([u.x * u.in_deg for u in v.innbs]))
v.h = v.h * v.out_deg
x = (1 - self.alpha_) * [v.h for v in g.dst_vertex] + self.alpha_ * feat0
return x