HyperGCN
- class dhg.models.HyperGCN(*args, **kwargs)[source]
Bases:
torch.nn.Module
The HyperGCN model proposed in HyperGCN: A New Method of Training Graph Convolutional Networks on Hypergraphs paper (NeurIPS 2019).
- 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_mediator (
str
) – Whether to use mediator to transform the hyperedges to edges in the graph. Defaults toFalse
.fast (
bool
) – If set toTrue
, the transformed graph structure will be computed once from the input hypergraph and vertex features, and cached for future use. Defaults toTrue
.drop_rate (
float
, optional) – Dropout ratio. Defaults to 0.5.