HNHNConv

class dhg.nn.HNHNConv(*args, **kwargs)

Bases: torch.nn.Module

The HNHN convolution layer proposed in HNHN: Hypergraph Networks with Hyperedge Neurons paper (ICML 2020).

Parameters

• in_channels (int) – \(C_{in}\) is the number of input channels.

• out_channels (int) – \(C_{out}\) is the number of output channels.

• bias (bool) – If set to False, the layer will not learn the bias parameter. Defaults to True.

• use_bn (bool) – If set to True, the layer will use batch normalization. Defaults to False.

• drop_rate (float) – If set to a positive number, the layer will use dropout. Defaults to 0.5.

• is_last (bool) – If set to True, the layer will not apply the final activation and dropout functions. Defaults to False.

forward(X, hg)

The forward function.

Parameters

• X (torch.Tensor) – Input vertex feature matrix. Size \((|\mathcal{V}|, C_{in})\).

• hg (dhg.Hypergraph) – The hypergraph structure that contains \(|\mathcal{V}|\) vertices.