easygraph.model.hypergraphs.hnhn module

class easygraph.model.hypergraphs.hnhn.HNHN(*args: Any, **kwargs: Any)

Bases: Module

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

Parameters:

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

• hid_channels (int) – \(C_{hid}\) is the number of hidden channels.

• num_classes (int) – The Number of class of the classification task.

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

• drop_rate (float, optional) – Dropout ratio. Defaults to 0.5.

forward(X: torch.Tensor, hg: eg.Hypergraph) → torch.Tensor

The forward function.

Parameters:

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

• hg (eg.Hypergraph) – The hypergraph structure that contains \(N\) vertices.