chore: import upstream snapshot with attribution

2026-07-13 12:36:30 +08:00
commit 55ab4e4a73
473 changed files with 72932 additions and 0 deletions
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+import torch
+import torch.nn as nn
+
+from easygraph.classes import Hypergraph
+
+
+class JHConv(nn.Module):
+    r"""The Jump Hypergraph Convolution layer proposed in `Dual Channel Hypergraph Collaborative Filtering <https://dl.acm.org/doi/10.1145/3394486.3403253>`_ paper (KDD 2020).
+
+    Matrix Format:
+
+    .. math::
+        \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``): :math:`C_{in}` is the number of input channels.
+        ``out_channels`` (int): :math:`C_{out}` is the number of output channels.
+        ``bias`` (``bool``): If set to ``False``, the layer will not learn the bias parameter. Defaults to ``True``.
+        ``use_bn`` (``bool``): If set to ``True``, the layer will use batch normalization. Defaults to ``False``.
+        ``drop_rate`` (``float``): If set to a positive number, the layer will use dropout. Defaults to ``0.5``.
+        ``is_last`` (``bool``): If set to ``True``, the layer will not apply the final activation and dropout functions. Defaults to ``False``.
+    """
+
+    def __init__(
+        self,
+        in_channels: int,
+        out_channels: int,
+        bias: bool = True,
+        use_bn: bool = False,
+        drop_rate: float = 0.5,
+        is_last: bool = False,
+    ):
+        super().__init__()
+        self.is_last = is_last
+        self.bn = nn.BatchNorm1d(out_channels) if use_bn else None
+        self.act = nn.ReLU(inplace=True)
+        self.drop = nn.Dropout(drop_rate)
+        self.theta = nn.Linear(in_channels, out_channels, bias=bias)
+
+    def forward(self, X: torch.Tensor, hg: Hypergraph) -> torch.Tensor:
+        r"""The forward function.
+
+        Parameters:
+            X (``torch.Tensor``): Input vertex feature matrix. Size :math:`(N, C_{in})`.
+            hg (``eg.Hypergraph``): The hypergraph structure that contains :math:`N` vertices.
+        """
+        X = self.theta(X)
+        if self.bn is not None:
+            X = self.bn(X)
+        X = hg.smoothing_with_HGNN(X) + X
+        if not self.is_last:
+            X = self.drop(self.act(X))
+        return X