dmlc--dgl
Tianjun Xiao
69f5869f3b
* go through gcn, relgcn * fix tagconv formula * fix doc in sageconv * fix sgconv doc * replace hat with tilde * more comments on gmmconv * fix agnnconv chebconv doc * modify nnconv doc * remove & * add nn conv examples * Rebase master * More merge conflicts * check homo * add back self loop for some convs, check homo in tranform * add example for denseconv * add example and doc for dotgat and cfconv * check in-degree for graphconv * add language fix * gconv address all comments * another round of change based on api template * change agnn * go through agnn, appnp, atomic, cf, cheb, dense, gat, sage modules * finish pytorch part of nn conv * mxnet graphconv done * tensorflow graphconv works * add new modules into doc * add comments to not split code * refine doc * resr * more comments * more fix * finish conv and dense conv part api * pylint fix * fix pylink * fix pylint * more fix * fix * fix test fail because zere in degree * fix test fail * sage is not update for mxnet tf Co-authored-by: Ubuntu <ubuntu@ip-172-31-0-81.us-east-2.compute.internal>
108 行
3.5 KiB
Python
108 行
3.5 KiB
Python
"""Torch Module for APPNPConv"""
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# pylint: disable= no-member, arguments-differ, invalid-name
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import torch as th
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from torch import nn
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from .... import function as fn
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class APPNPConv(nn.Module):
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r"""
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Description
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-----------
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Approximate Personalized Propagation of Neural Predictions
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layer from paper `Predict then Propagate: Graph Neural Networks
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meet Personalized PageRank <https://arxiv.org/pdf/1810.05997.pdf>`__.
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.. math::
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H^{0} &= X
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H^{l+1} &= (1-\alpha)\left(\tilde{D}^{-1/2}
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\tilde{A} \tilde{D}^{-1/2} H^{l}\right) + \alpha H^{0}
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where :math:`\tilde{A}` is :math:`A` + :math:`I`.
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Parameters
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----------
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k : int
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The number of iterations :math:`K`.
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alpha : float
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The teleport probability :math:`\alpha`.
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edge_drop : float, optional
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The dropout rate on edges that controls the
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messages received by each node. Default: ``0``.
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Example
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-------
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>>> import dgl
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>>> import numpy as np
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>>> import torch as th
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>>> from dgl.nn import APPNPConv
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>>>
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>>> g = dgl.graph(([0,1,2,3,2,5], [1,2,3,4,0,3]))
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>>> feat = th.ones(6, 10)
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>>> conv = APPNPConv(k=3, alpha=0.5)
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>>> res = conv(g, feat)
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>>> res
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tensor([[1.0000, 1.0000, 1.0000, 1.0000, 1.0000, 1.0000, 1.0000, 1.0000, 1.0000,
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1.0000],
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[1.0000, 1.0000, 1.0000, 1.0000, 1.0000, 1.0000, 1.0000, 1.0000, 1.0000,
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1.0000],
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[1.0000, 1.0000, 1.0000, 1.0000, 1.0000, 1.0000, 1.0000, 1.0000, 1.0000,
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1.0000],
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[1.0303, 1.0303, 1.0303, 1.0303, 1.0303, 1.0303, 1.0303, 1.0303, 1.0303,
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1.0303],
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[0.8643, 0.8643, 0.8643, 0.8643, 0.8643, 0.8643, 0.8643, 0.8643, 0.8643,
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0.8643],
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[0.5000, 0.5000, 0.5000, 0.5000, 0.5000, 0.5000, 0.5000, 0.5000, 0.5000,
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0.5000]])
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"""
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def __init__(self,
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k,
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alpha,
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edge_drop=0.):
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super(APPNPConv, self).__init__()
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self._k = k
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self._alpha = alpha
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self.edge_drop = nn.Dropout(edge_drop)
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def forward(self, graph, feat):
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r"""
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Description
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-----------
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Compute APPNP layer.
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Parameters
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----------
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graph : DGLGraph
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The graph.
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feat : torch.Tensor
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The input feature of shape :math:`(N, *)`. :math:`N` is the
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number of nodes, and :math:`*` could be of any shape.
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Returns
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-------
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torch.Tensor
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The output feature of shape :math:`(N, *)` where :math:`*`
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should be the same as input shape.
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"""
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with graph.local_scope():
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norm = th.pow(graph.in_degrees().float().clamp(min=1), -0.5)
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shp = norm.shape + (1,) * (feat.dim() - 1)
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norm = th.reshape(norm, shp).to(feat.device)
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feat_0 = feat
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for _ in range(self._k):
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# normalization by src node
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feat = feat * norm
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graph.ndata['h'] = feat
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graph.edata['w'] = self.edge_drop(
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th.ones(graph.number_of_edges(), 1).to(feat.device))
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graph.update_all(fn.u_mul_e('h', 'w', 'm'),
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fn.sum('m', 'h'))
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feat = graph.ndata.pop('h')
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# normalization by dst node
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feat = feat * norm
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feat = (1 - self._alpha) * feat + self._alpha * feat_0
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return feat
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