Source code for torch_geometric.nn.conv.mixhop_conv

from typing import List, Optional

import torch
from torch import Tensor
from torch.nn import Parameter

from torch_geometric.nn.conv import MessagePassing
from torch_geometric.nn.conv.gcn_conv import gcn_norm
from torch_geometric.nn.dense.linear import Linear
from torch_geometric.nn.inits import zeros
from torch_geometric.typing import Adj, OptTensor, SparseTensor
from torch_geometric.utils import spmm

[docs]class MixHopConv(MessagePassing):
r"""The Mix-Hop graph convolutional operator from the
"MixHop: Higher-Order Graph Convolutional Architecturesvia Sparsified
Neighborhood Mixing" <https://arxiv.org/abs/1905.00067>_ paper.

.. math::
\mathbf{X}^{\prime}={\Bigg\Vert}_{p\in P}
{\left( \mathbf{\hat{D}}^{-1/2} \mathbf{\hat{A}}
\mathbf{\hat{D}}^{-1/2} \right)}^p \mathbf{X} \mathbf{\Theta},

where :math:\mathbf{\hat{A}} = \mathbf{A} + \mathbf{I} denotes the
adjacency matrix with inserted self-loops and
:math:\hat{D}_{ii} = \sum_{j=0} \hat{A}_{ij} its diagonal degree matrix.

Args:
in_channels (int): Size of each input sample, or :obj:-1 to derive
the size from the first input(s) to the forward method.
out_channels (int): Size of each output sample.
powers (List[int], optional): The powers of the adjacency matrix to
use. (default: :obj:[0, 1, 2])
add_self_loops (bool, optional): If set to :obj:False, will not add
self-loops to the input graph. (default: :obj:True)
bias (bool, optional): If set to :obj:False, the layer will not learn
an additive bias. (default: :obj:True)
:class:torch_geometric.nn.conv.MessagePassing.

Shapes:
- **input:**
node features :math:(|\mathcal{V}|, F_{in}),
edge indices :math:(2, |\mathcal{E}|),
edge weights :math:(|\mathcal{E}|) *(optional)*
- **output:**
node features :math:(|\mathcal{V}|, |P| \cdot F_{out})
"""
def __init__(
self,
in_channels: int,
out_channels: int,
powers: Optional[List[int]] = None,
bias: bool = True,
**kwargs,
):
super().__init__(**kwargs)

if powers is None:
powers = [0, 1, 2]

self.in_channels = in_channels
self.out_channels = out_channels
self.powers = powers

self.lins = torch.nn.ModuleList([
Linear(in_channels, out_channels, bias=False)
if p in powers else torch.nn.Identity()
for p in range(max(powers) + 1)
])

if bias:
self.bias = Parameter(torch.empty(len(powers) * out_channels))
else:
self.register_parameter('bias', None)

self.reset_parameters()

[docs]    def reset_parameters(self):
for lin in self.lins:
if hasattr(lin, 'reset_parameters'):
lin.reset_parameters()
zeros(self.bias)

[docs]    def forward(self, x: Tensor, edge_index: Adj,
edge_weight: OptTensor = None) -> Tensor:

if isinstance(edge_index, Tensor):
edge_index, edge_weight = gcn_norm(  # yapf: disable
edge_index, edge_weight, x.size(self.node_dim), False,
elif isinstance(edge_index, SparseTensor):
edge_index = gcn_norm(  # yapf: disable
edge_index, edge_weight, x.size(self.node_dim), False,

outs = [self.lins[0](x)]

for lin in self.lins[1:]:
# propagate_type: (x: Tensor, edge_weight: OptTensor)
x = self.propagate(edge_index, x=x, edge_weight=edge_weight)

outs.append(lin.forward(x))

out = torch.cat([outs[p] for p in self.powers], dim=-1)

if self.bias is not None:
out = out + self.bias

return out

def message(self, x_j: Tensor, edge_weight: OptTensor) -> Tensor:
return x_j if edge_weight is None else edge_weight.view(-1, 1) * x_j