DenseChebConv¶

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

Bases: torch.nn.modules.module.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>)