Source code for easygraph.nn.convs.hypergraphs.hypergcn_conv

from typing import Optional

import torch
import torch.nn as nn

from easygraph.classes import Graph
from easygraph.classes import Hypergraph


[docs] class HyperGCNConv(nn.Module): r"""The HyperGCN convolution layer 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. ``out_channels`` (int): :math:`C_{out}` is the number of output channels. ``use_mediator`` (``str``): Whether to use mediator to transform the hyperedges to edges in the graph. Defaults to ``False``. ``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, use_mediator: bool = False, 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.use_mediator = use_mediator self.act = nn.ReLU(inplace=True) self.drop = nn.Dropout(drop_rate) self.theta = nn.Linear(in_channels, out_channels, bias=bias)
[docs] def forward( self, X: torch.Tensor, hg: Hypergraph, cached_g: Optional[Graph] = None ) -> 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. ``cached_g`` (``eg.Graph``): The pre-transformed graph structure from the hypergraph structure that contains :math:`N` vertices. If not provided, the graph structure will be transformed for each forward time. Defaults to ``None``. """ X = self.theta(X) if self.bn is not None: X = self.bn(X) if cached_g is None: g = Graph.from_hypergraph_hypergcn(hg, X, self.use_mediator) X = g.smoothing_with_GCN(X) else: X = cached_g.smoothing_with_GCN(X) if not self.is_last: X = self.drop(self.act(X)) return X