DenseGraphConv¶
-
class
dgl.nn.mxnet.conv.
DenseGraphConv
(in_feats, out_feats, norm='both', bias=True, activation=None)[source]¶ Bases:
mxnet.gluon.block.Block
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.
See also
-
forward
(adj, feat)[source]¶ Compute (Dense) Graph Convolution layer.
- Parameters
adj (mxnet.NDArray) – 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 (mxnet.NDArray) – The input feature.
- Returns
The output feature of shape \((N, D_{out})\) where \(D_{out}\) is size of output feature.
- Return type
mxnet.NDArray