DenseChebConv

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

Bases: Module

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.

Example

>>> import dgl
>>> import numpy as np
>>> import torch as th
>>> from dgl.nn import DenseChebConv
>>>
>>> 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 = DenseChebConv(10, 2, 2)
>>> res = conv(adj, feat)
>>> res
tensor([[-3.3516, -2.4797],
        [-3.3516, -2.4797],
        [-3.3516, -2.4797],
        [-4.5192, -3.0835],
        [-2.5259, -2.0527],
        [-0.5327, -1.0219]], grad_fn=<AddBackward0>)