Source code for torch_geometric.nn.conv.wl_conv_continuous

from typing import Union

from torch import Tensor

from torch_geometric.nn.conv import MessagePassing
from torch_geometric.typing import (
from torch_geometric.utils import scatter, spmm

[docs]class WLConvContinuous(MessagePassing): r"""The Weisfeiler Lehman operator from the `"Wasserstein Weisfeiler-Lehman Graph Kernels" <>`_ paper. Refinement is done though a degree-scaled mean aggregation and works on nodes with continuous attributes: .. math:: \mathbf{x}^{\prime}_i = \frac{1}{2}\big(\mathbf{x}_i + \frac{1}{\textrm{deg}(i)} \sum_{j \in \mathcal{N}(i)} e_{j,i} \cdot \mathbf{x}_j \big) where :math:`e_{j,i}` denotes the edge weight from source node :obj:`j` to target node :obj:`i` (default: :obj:`1`) Args: **kwargs (optional): Additional arguments of :class:`torch_geometric.nn.conv.MessagePassing`. Shapes: - **input:** node features :math:`(|\mathcal{V}|, F)` or :math:`((|\mathcal{V_s}|, F), (|\mathcal{V_t}|, F))` if bipartite, edge indices :math:`(2, |\mathcal{E}|)`, edge weights :math:`(|\mathcal{E}|)` *(optional)* - **output:** node features :math:`(|\mathcal{V}|, F)` or :math:`(|\mathcal{V}_t|, F)` if bipartite """ def __init__(self, **kwargs): super().__init__(aggr='add', **kwargs)
[docs] def forward( self, x: Union[Tensor, OptPairTensor], edge_index: Adj, edge_weight: OptTensor = None, size: Size = None, ) -> Tensor: if isinstance(x, Tensor): x = (x, x) # propagate_type: (x: OptPairTensor, edge_weight: OptTensor) out = self.propagate(edge_index, x=x, edge_weight=edge_weight, size=size) if isinstance(edge_index, SparseTensor): assert edge_weight is None dst_index, _, edge_weight = edge_index.coo() else: dst_index = edge_index[1] if edge_weight is None: edge_weight = x[0].new_ones(dst_index.numel()) deg = scatter(edge_weight, dst_index, 0, out.size(0), reduce='sum') deg_inv = 1. / deg deg_inv.masked_fill_(deg_inv == float('inf'), 0) out = deg_inv.view(-1, 1) * out x_dst = x[1] if x_dst is not None: out = 0.5 * (x_dst + out) 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 def message_and_aggregate(self, adj_t: Adj, x: OptPairTensor) -> Tensor: return spmm(adj_t, x[0], reduce=self.aggr)