HGNNPConv
- class dhg.nn.HGNNPConv(*args, **kwargs)[source]
Bases:
torch.nn.ModuleThe HGNN + convolution layer proposed in HGNN+: General Hypergraph Neural Networks paper (IEEE T-PAMI 2022).
Sparse Format:
\[\begin{split}\left\{ \begin{aligned} m_{\beta}^{t} &=\sum_{\alpha \in \mathcal{N}_{v}(\beta)} M_{v}^{t}\left(x_{\alpha}^{t}\right) \\ y_{\beta}^{t} &=U_{e}^{t}\left(w_{\beta}, m_{\beta}^{t}\right) \\ m_{\alpha}^{t+1} &=\sum_{\beta \in \mathcal{N}_{e}(\alpha)} M_{e}^{t}\left(x_{\alpha}^{t}, y_{\beta}^{t}\right) \\ x_{\alpha}^{t+1} &=U_{v}^{t}\left(x_{\alpha}^{t}, m_{\alpha}^{t+1}\right) \\ \end{aligned} \right.\end{split}\]Matrix Format:
\[\mathbf{X}^{\prime} = \sigma \left( \mathbf{D}_v^{-1} \mathbf{H} \mathbf{W}_e \mathbf{D}_e^{-1} \mathbf{H}^\top \mathbf{X} \mathbf{\Theta} \right).\]- 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.