Source code for torch_geometric.nn.conv.lg_conv

from torch import Tensor
from torch_sparse import SparseTensor, matmul

from torch_geometric.nn.conv import MessagePassing
from torch_geometric.nn.conv.gcn_conv import gcn_norm
from torch_geometric.typing import Adj, OptTensor

[docs]class LGConv(MessagePassing): r"""The Light Graph Convolution (LGC) operator from the `"LightGCN: Simplifying and Powering Graph Convolution Network for Recommendation" <>`_ paper .. math:: \mathbf{x}^{\prime}_i = \sum_{j \in \mathcal{N}(i)} \frac{e_{j,i}}{\sqrt{\deg(i)\deg(j)}} \mathbf{x}_j Args: normalize (bool, optional): If set to :obj:`False`, output features will not be normalized via symmetric normalization. (default: :obj:`True`) **kwargs (optional): Additional arguments of :class:`torch_geometric.nn.conv.MessagePassing`. Shapes: - **input:** node features :math:`(|\mathcal{V}|, F)`, edge indices :math:`(2, |\mathcal{E}|)`, edge weights :math:`(|\mathcal{E}|)` *(optional)* - **output:** node features :math:`(|\mathcal{V}|, F)` """ def __init__(self, normalize: bool = True, **kwargs): kwargs.setdefault('aggr', 'add') super().__init__(**kwargs) self.normalize = normalize
[docs] def reset_parameters(self): pass
[docs] def forward(self, x: Tensor, edge_index: Adj, edge_weight: OptTensor = None) -> Tensor: """""" if self.normalize and isinstance(edge_index, Tensor): out = gcn_norm(edge_index, edge_weight, x.size(self.node_dim), add_self_loops=False, dtype=x.dtype) edge_index, edge_weight = out elif self.normalize and isinstance(edge_index, SparseTensor): edge_index = gcn_norm(edge_index, None, x.size(self.node_dim), add_self_loops=False, dtype=x.dtype) # propagate_type: (x: Tensor, edge_weight: OptTensor) return self.propagate(edge_index, x=x, edge_weight=edge_weight, size=None)
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 def message_and_aggregate(self, adj_t: SparseTensor, x: Tensor) -> Tensor: return matmul(adj_t, x, reduce=self.aggr)