wehub-resource-sync
55 lines
2.2 KiB
Python
55 lines
2.2 KiB
Python
import torch
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import torch.nn as nn
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from easygraph.classes import Hypergraph
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class JHConv(nn.Module):
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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).
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Matrix Format:
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.. math::
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\mathbf{X}^{\prime} = \sigma \left( \mathbf{D}_v^{-\frac{1}{2}} \mathbf{H} \mathbf{W}_e \mathbf{D}_e^{-1}
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\mathbf{H}^\top \mathbf{D}_v^{-\frac{1}{2}} \mathbf{X} \mathbf{\Theta} + \mathbf{X} \right).
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Parameters:
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``in_channels`` (``int``): :math:`C_{in}` is the number of input channels.
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``out_channels`` (int): :math:`C_{out}` is the number of output channels.
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``bias`` (``bool``): If set to ``False``, the layer will not learn the bias parameter. Defaults to ``True``.
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``use_bn`` (``bool``): If set to ``True``, the layer will use batch normalization. Defaults to ``False``.
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``drop_rate`` (``float``): If set to a positive number, the layer will use dropout. Defaults to ``0.5``.
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``is_last`` (``bool``): If set to ``True``, the layer will not apply the final activation and dropout functions. Defaults to ``False``.
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"""
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def __init__(
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self,
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in_channels: int,
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out_channels: int,
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bias: bool = True,
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use_bn: bool = False,
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drop_rate: float = 0.5,
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is_last: bool = False,
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):
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super().__init__()
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self.is_last = is_last
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self.bn = nn.BatchNorm1d(out_channels) if use_bn else None
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self.act = nn.ReLU(inplace=True)
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self.drop = nn.Dropout(drop_rate)
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self.theta = nn.Linear(in_channels, out_channels, bias=bias)
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def forward(self, X: torch.Tensor, hg: Hypergraph) -> torch.Tensor:
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r"""The forward function.
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Parameters:
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X (``torch.Tensor``): Input vertex feature matrix. Size :math:`(N, C_{in})`.
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hg (``eg.Hypergraph``): The hypergraph structure that contains :math:`N` vertices.
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"""
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X = self.theta(X)
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if self.bn is not None:
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X = self.bn(X)
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X = hg.smoothing_with_HGNN(X) + X
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if not self.is_last:
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X = self.drop(self.act(X))
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return X
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