Source code for mindspore_gl.nn.conv.appnpconv

# 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
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
# ============================================================================
"""APPNPConv Layer."""
import mindspore as ms
from mindspore import Tensor
from mindspore._checkparam import Validator
from mindspore_gl import Graph

from .. import GNNCell


[docs]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): 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 :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] 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) """ def __init__(self, k: int, alpha: float, edge_drop=1.0) -> None: super().__init__() self.k_ = Validator.check_positive_int(k, "k", self.cls_name) self.alpha_ = Validator.check_is_float(alpha, "alpha", self.cls_name) 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_}.") edge_drop = Validator.check_is_float(edge_drop, "edge_drop", self.cls_name) 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(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