wehub-resource-sync
59 lines
2.1 KiB
Python
59 lines
2.1 KiB
Python
import torch
|
|
import torch.nn as nn
|
|
|
|
from easygraph.classes import Hypergraph
|
|
|
|
|
|
class HWNNConv(nn.Module):
|
|
r"""The HWNNConv model proposed in `Heterogeneous Hypergraph Embedding for Graph Classification <https://arxiv.org/pdf/2010.10728>`_ paper (WSDM 2021).
|
|
|
|
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.
|
|
``ncount`` (``int``): The Number of node in the hypergraph.
|
|
``K1`` (``int``): Polynomial calculation times.
|
|
``K2`` (``int``): Polynomial calculation times.
|
|
``approx`` (``bool``): Whether to use polynomial fitting
|
|
"""
|
|
|
|
def __init__(
|
|
self,
|
|
in_channels: int,
|
|
out_channels: int,
|
|
ncount: int,
|
|
K1: int = 2,
|
|
K2: int = 2,
|
|
approx: bool = False,
|
|
):
|
|
super().__init__()
|
|
self.in_channels = in_channels
|
|
self.out_channels = out_channels
|
|
self.K1 = K1
|
|
self.K2 = K2
|
|
self.ncount = ncount
|
|
self.approx = approx
|
|
self.W = torch.nn.Parameter(torch.Tensor(self.in_channels, self.out_channels))
|
|
self.W_d = torch.nn.Parameter(torch.Tensor(self.ncount))
|
|
self.par = torch.nn.Parameter(torch.Tensor(self.K1 + self.K2))
|
|
self.init_parameters()
|
|
|
|
def init_parameters(self):
|
|
torch.nn.init.xavier_uniform_(self.W)
|
|
torch.nn.init.uniform_(self.W_d, 0.99, 1.01)
|
|
torch.nn.init.uniform_(self.par, 0, 0.99)
|
|
|
|
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.
|
|
"""
|
|
if self.approx == True:
|
|
X = hg.smoothing_with_HWNN_approx(
|
|
X, self.par, self.W_d, self.K1, self.K2, self.W
|
|
)
|
|
else:
|
|
X = hg.smoothing_with_HWNN_wavelet(X, self.W_d, self.W)
|
|
return X
|