import torch
import torch.nn as nn
import dhg
from dhg.nn import HyperGCNConv
from dhg.structure.graphs import Graph
[docs]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).
Args:
``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
)
)
[docs] def forward(self, X: torch.Tensor, hg: "dhg.Hypergraph") -> torch.Tensor:
r"""The forward function.
Args:
``X`` (``torch.Tensor``): Input vertex feature matrix. Size :math:`(N, C_{in})`.
``hg`` (``dhg.Hypergraph``): The hypergraph structure that contains :math:`N` vertices.
"""
if self.fast:
if self.cached_g is None:
self.cached_g = Graph.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