easygraph.nn.convs.hypergraphs.hwnn_conv module#

class easygraph.nn.convs.hypergraphs.hwnn_conv.HWNNConv(*args: Any, **kwargs: Any)[source]#

Bases: Module

The HWNNConv model proposed in Heterogeneous Hypergraph Embedding for Graph Classification paper (WSDM 2021).

Parameters:

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

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

  • ncount (int) – The Number of node in the hypergraph.

  • K1 (int) – Polynomial calculation times.

  • K2 (int) – Polynomial calculation times.

  • approx (bool) – Whether to use polynomial fitting

forward(X: torch.Tensor, hg: Hypergraph) → torch.Tensor[source]#

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.

init_parameters()[source]#