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
@@ -0,0 +1,67 @@
+import torch
+import torch.nn as nn
+
+from easygraph.classes import Hypergraph
+from easygraph.nn import HyperGCNConv
+
+
+class HyperGCN(nn.Module):
+    r"""The HyperGCN model proposed in `HyperGCN: A New Method of Training Graph Convolutional Networks on Hypergraphs <https://papers.nips.cc/paper/2019/file/1efa39bcaec6f3900149160693694536-Paper.pdf>`_ paper (NeurIPS 2019).
+
+    Parameters:
+        ``in_channels`` (``int``): :math:`C_{in}` is the number of input channels.
+        ``hid_channels`` (``int``): :math:`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 to ``False``.
+        ``fast`` (``bool``): If set to ``True``, the transformed graph structure will be computed once from the input hypergraph and vertex features, and cached for future use. Defaults to ``True``.
+        ``drop_rate`` (``float``, optional): Dropout ratio. Defaults to 0.5.
+    """
+
+    def __init__(
+        self,
+        in_channels: int,
+        hid_channels: int,
+        num_classes: int,
+        use_mediator: bool = False,
+        use_bn: bool = False,
+        fast: bool = True,
+        drop_rate: float = 0.5,
+    ) -> None:
+        super().__init__()
+        self.fast = fast
+        self.cached_g = None
+        self.with_mediator = use_mediator
+        self.layers = nn.ModuleList()
+        self.layers.append(
+            HyperGCNConv(
+                in_channels,
+                hid_channels,
+                use_mediator,
+                use_bn=use_bn,
+                drop_rate=drop_rate,
+            )
+        )
+        self.layers.append(
+            HyperGCNConv(
+                hid_channels, num_classes, use_mediator, use_bn=use_bn, is_last=True
+            )
+        )
+
+    def forward(self, X: torch.Tensor, hg: "eg.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.
+        """
+        if self.fast:
+            if self.cached_g is None:
+                self.cached_g = Hypergraph.from_hypergraph_hypergcn(
+                    hg, X, self.with_mediator
+                )
+            for layer in self.layers:
+                X = layer(X, hg, self.cached_g)
+        else:
+            for layer in self.layers:
+                X = layer(X, hg)
+        return X