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>)