DenseGraphConv¶
-
class
dgl.nn.pytorch.conv.
DenseGraphConv
(in_feats, out_feats, norm='both', bias=True, activation=None)[source]¶ Bases:
torch.nn.modules.module.Module
Graph Convolutional layer from Semi-Supervised Classification with Graph Convolutional Networks
We recommend user to use this module when applying graph convolution on dense graphs.
- Parameters
in_feats (int) – Input feature size; i.e, the number of dimensions of \(h_j^{(l)}\).
out_feats (int) – Output feature size; i.e., the number of dimensions of \(h_i^{(l+1)}\).
norm (str, optional) – How to apply the normalizer. If is ‘right’, divide the aggregated messages by each node’s in-degrees, which is equivalent to averaging the received messages. If is ‘none’, no normalization is applied. Default is ‘both’, where the \(c_{ij}\) in the paper is applied.
bias (bool, optional) – If True, adds a learnable bias to the output. Default:
True
.activation (callable activation function/layer or None, optional) – If not None, applies an activation function to the updated node features. Default:
None
.
Notes
Zero in-degree nodes will lead to all-zero output. A common practice to avoid this is to add a self-loop for each node in the graph, which can be achieved by setting the diagonal of the adjacency matrix to be 1.
Example
>>> import dgl >>> import numpy as np >>> import torch as th >>> from dgl.nn import DenseGraphConv >>> >>> feat = th.ones(6, 10) >>> adj = th.tensor([[0., 0., 1., 0., 0., 0.], ... [1., 0., 0., 0., 0., 0.], ... [0., 1., 0., 0., 0., 0.], ... [0., 0., 1., 0., 0., 1.], ... [0., 0., 0., 1., 0., 0.], ... [0., 0., 0., 0., 0., 0.]]) >>> conv = DenseGraphConv(10, 2) >>> res = conv(adj, feat) >>> res tensor([[0.2159, 1.9027], [0.3053, 2.6908], [0.3053, 2.6908], [0.3685, 3.2481], [0.3053, 2.6908], [0.0000, 0.0000]], grad_fn=<AddBackward0>)
See also
-
forward
(adj, feat)[source]¶ Compute (Dense) Graph Convolution layer.
- Parameters
adj (torch.Tensor) – The adjacency matrix of the graph to apply Graph Convolution on, when applied to a unidirectional bipartite graph,
adj
should be of shape should be of shape \((N_{out}, N_{in})\); when applied to a homo graph,adj
should be of shape \((N, N)\). In both cases, a row represents a destination node while a column represents a source node.feat (torch.Tensor) – The input feature.
- Returns
The output feature of shape \((N, D_{out})\) where \(D_{out}\) is size of output feature.
- Return type
torch.Tensor