JHConv
- class dhg.nn.JHConv(*args, **kwargs)[source]
Bases:
torch.nn.ModuleThe Jump Hypergraph Convolution layer proposed in Dual Channel Hypergraph Collaborative Filtering paper (KDD 2020).
Matrix Format:
\[\mathbf{X}^{\prime} = \sigma \left( \mathbf{D}_v^{-\frac{1}{2}} \mathbf{H} \mathbf{W}_e \mathbf{D}_e^{-1} \mathbf{H}^\top \mathbf{D}_v^{-\frac{1}{2}} \mathbf{X} \mathbf{\Theta} + \mathbf{X} \right).\]- 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 toFalse, the layer will not learn the bias parameter. Defaults toTrue.use_bn (
bool) – If set toTrue, the layer will use batch normalization. Defaults toFalse.drop_rate (
float) – If set to a positive number, the layer will use dropout. Defaults to0.5.is_last (
bool) – If set toTrue, the layer will not apply the final activation and dropout functions. Defaults toFalse.