Source code for dgl.nn.mxnet.conv.densesageconv

"""MXNet Module for DenseGraphSAGE"""
# pylint: disable= no-member, arguments-differ, invalid-name
import math

import mxnet as mx
from mxnet import nd
from mxnet.gluon import nn

from ....utils import check_eq_shape


[docs]class DenseSAGEConv(nn.Block): """GraphSAGE layer from `Inductive Representation Learning on Large Graphs <https://arxiv.org/abs/1706.02216>`__ We recommend to use this module when appying GraphSAGE on dense graphs. Note that we only support gcn aggregator in DenseSAGEConv. Parameters ---------- in_feats : int Input feature size; i.e, the number of dimensions of :math:`h_i^{(l)}`. out_feats : int Output feature size; i.e, the number of dimensions of :math:`h_i^{(l+1)}`. feat_drop : float, optional Dropout rate on features. Default: 0. bias : bool If True, adds a learnable bias to the output. Default: ``True``. norm : callable activation function/layer or None, optional If not None, applies normalization to the updated node features. activation : callable activation function/layer or None, optional If not None, applies an activation function to the updated node features. Default: ``None``. See also -------- `SAGEConv <https://docs.dgl.ai/api/python/nn.pytorch.html#sageconv>`__ """ def __init__( self, in_feats, out_feats, feat_drop=0.0, bias=True, norm=None, activation=None, ): super(DenseSAGEConv, self).__init__() self._in_feats = in_feats self._out_feats = out_feats self._norm = norm with self.name_scope(): self.feat_drop = nn.Dropout(feat_drop) self.activation = activation self.fc = nn.Dense( out_feats, in_units=in_feats, use_bias=bias, weight_initializer=mx.init.Xavier(magnitude=math.sqrt(2.0)), )
[docs] def forward(self, adj, feat): r""" Description ----------- Compute (Dense) Graph SAGE layer. Parameters ---------- adj : mxnet.NDArray The adjacency matrix of the graph to apply SAGE Convolution on, when applied to a unidirectional bipartite graph, ``adj`` should be of shape should be of shape :math:`(N_{out}, N_{in})`; when applied to a homo graph, ``adj`` should be of shape :math:`(N, N)`. In both cases, a row represents a destination node while a column represents a source node. feat : mxnet.NDArray or a pair of mxnet.NDArray If a mxnet.NDArray is given, the input feature of shape :math:`(N, D_{in})` where :math:`D_{in}` is size of input feature, :math:`N` is the number of nodes. If a pair of mxnet.NDArray is given, the pair must contain two tensors of shape :math:`(N_{in}, D_{in})` and :math:`(N_{out}, D_{in})`. Returns ------- mxnet.NDArray The output feature of shape :math:`(N, D_{out})` where :math:`D_{out}` is size of output feature. """ check_eq_shape(feat) if isinstance(feat, tuple): feat_src = self.feat_drop(feat[0]) feat_dst = self.feat_drop(feat[1]) else: feat_src = feat_dst = self.feat_drop(feat) adj = adj.astype(feat_src.dtype).as_in_context(feat_src.context) in_degrees = adj.sum(axis=1, keepdims=True) h_neigh = (nd.dot(adj, feat_src) + feat_dst) / (in_degrees + 1) rst = self.fc(h_neigh) # activation if self.activation is not None: rst = self.activation(rst) # normalization if self._norm is not None: rst = self._norm(rst) return rst