Source code for torch_geometric.nn.conv.appnp

from typing import Optional

import torch.nn.functional as F
from torch import Tensor

from torch_geometric.nn.conv import MessagePassing
from torch_geometric.nn.conv.gcn_conv import gcn_norm
from torch_geometric.typing import Adj, OptPairTensor, OptTensor, SparseTensor
from torch_geometric.utils import is_torch_sparse_tensor, spmm, to_edge_index
from torch_geometric.utils.sparse import set_sparse_value

[docs]class APPNP(MessagePassing): r"""The approximate personalized propagation of neural predictions layer from the `"Predict then Propagate: Graph Neural Networks meet Personalized PageRank" <>`_ paper. .. math:: \mathbf{X}^{(0)} &= \mathbf{X} \mathbf{X}^{(k)} &= (1 - \alpha) \mathbf{\hat{D}}^{-1/2} \mathbf{\hat{A}} \mathbf{\hat{D}}^{-1/2} \mathbf{X}^{(k-1)} + \alpha \mathbf{X}^{(0)} \mathbf{X}^{\prime} &= \mathbf{X}^{(K)}, 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. The adjacency matrix can include other values than :obj:`1` representing edge weights via the optional :obj:`edge_weight` tensor. Args: K (int): Number of iterations :math:`K`. alpha (float): Teleport probability :math:`\alpha`. dropout (float, optional): Dropout probability of edges during training. (default: :obj:`0`) cached (bool, optional): If set to :obj:`True`, the layer will cache the computation of :math:`\mathbf{\hat{D}}^{-1/2} \mathbf{\hat{A}} \mathbf{\hat{D}}^{-1/2}` on first execution, and will use the cached version for further executions. This parameter should only be set to :obj:`True` in transductive learning scenarios. (default: :obj:`False`) add_self_loops (bool, optional): If set to :obj:`False`, will not add self-loops to the input graph. (default: :obj:`True`) normalize (bool, optional): Whether to add self-loops and apply 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)` """ _cached_edge_index: Optional[OptPairTensor] _cached_adj_t: Optional[SparseTensor] def __init__(self, K: int, alpha: float, dropout: float = 0., cached: bool = False, add_self_loops: bool = True, normalize: bool = True, **kwargs): kwargs.setdefault('aggr', 'add') super().__init__(**kwargs) self.K = K self.alpha = alpha self.dropout = dropout self.cached = cached self.add_self_loops = add_self_loops self.normalize = normalize self._cached_edge_index = None self._cached_adj_t = None
[docs] def reset_parameters(self): super().reset_parameters() self._cached_edge_index = None self._cached_adj_t = None
[docs] def forward( self, x: Tensor, edge_index: Adj, edge_weight: OptTensor = None, ) -> Tensor: if self.normalize: if isinstance(edge_index, Tensor): cache = self._cached_edge_index if cache is None: edge_index, edge_weight = gcn_norm( # yapf: disable edge_index, edge_weight, x.size(self.node_dim), False, self.add_self_loops, self.flow, dtype=x.dtype) if self.cached: self._cached_edge_index = (edge_index, edge_weight) else: edge_index, edge_weight = cache[0], cache[1] elif isinstance(edge_index, SparseTensor): cache = self._cached_adj_t if cache is None: edge_index = gcn_norm( # yapf: disable edge_index, edge_weight, x.size(self.node_dim), False, self.add_self_loops, self.flow, dtype=x.dtype) if self.cached: self._cached_adj_t = edge_index else: edge_index = cache h = x for k in range(self.K): if self.dropout > 0 and if isinstance(edge_index, Tensor): if is_torch_sparse_tensor(edge_index): _, edge_weight = to_edge_index(edge_index) edge_weight = F.dropout(edge_weight, p=self.dropout) edge_index = set_sparse_value(edge_index, edge_weight) else: assert edge_weight is not None edge_weight = F.dropout(edge_weight, p=self.dropout) else: value = assert value is not None value = F.dropout(value, p=self.dropout) edge_index = edge_index.set_value(value, layout='coo') # propagate_type: (x: Tensor, edge_weight: OptTensor) x = self.propagate(edge_index, x=x, edge_weight=edge_weight) x = x * (1 - self.alpha) x = x + self.alpha * h return x
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: Tensor) -> Tensor: return spmm(adj_t, x, reduce=self.aggr) def __repr__(self) -> str: return f'{self.__class__.__name__}(K={self.K}, alpha={self.alpha})'