# DenseGraphConvยถ

class dgl.nn.mxnet.conv.DenseGraphConv(in_feats, out_feats, norm='both', bias=True, activation=None)[source]ยถ

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) โ Input feature size; i.e, the number of dimensions of $$h_j^{(l)}$$.

• out_feats (int) โ Output feature size; i.e., the number of dimensions of $$h_i^{(l+1)}$$.

• norm (str, optional) โ How to apply the normalizer. If is โrightโ, divide the aggregated messages by each nodeโs in-degrees, which is equivalent to averaging the received messages. If is โnoneโ, no normalization is applied. Default is โbothโ, where the $$c_{ij}$$ in the paper is applied.

• bias (bool, optional) โ If True, adds a learnable bias to the output. Default: True.

• activation (callable activation function/layer or None, optional) โ 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.

GraphConv

forward(adj, feat)[source]ยถ

Compute (Dense) Graph Convolution layer.

Parameters
• adj (mxnet.NDArray) โ 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) โ The input feature.

Returns

The output feature of shape $$(N, D_{out})$$ where $$D_{out}$$ is size of output feature.

Return type

mxnet.NDArray