# DenseChebConv¶

class dgl.nn.mxnet.conv.DenseChebConv(in_feats, out_feats, k, bias=True)[source]

Bases: mxnet.gluon.block.Block

Chebyshev Spectral Graph Convolution layer from Convolutional Neural Networks on Graphs with Fast Localized Spectral Filtering

We recommend to use this module when applying ChebConv on dense graphs.

Parameters
• in_feats (int) – Dimension of input features $$h_i^{(l)}$$.

• out_feats (int) – Dimension of output features $$h_i^{(l+1)}$$.

• k (int) – Chebyshev filter size.

• activation (function, optional) – Activation function, default is ReLu.

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

ChebConv

forward(adj, feat, lambda_max=None)[source]

Compute (Dense) Chebyshev Spectral Graph Convolution layer.

Parameters
• adj (mxnet.NDArray) – The adjacency matrix of the graph to apply Graph Convolution on, should be of shape $$(N, N)$$, where a row represents the destination and a column represents the source.

• feat (mxnet.NDArray) – The input feature of shape $$(N, D_{in})$$ where $$D_{in}$$ is size of input feature, $$N$$ is the number of nodes.

• lambda_max (float or None, optional) – A float value indicates the largest eigenvalue of given graph. Default: None.

Returns

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

Return type

mxnet.NDArray