torch_geometric.nn.conv.HGTConv

class HGTConv(in_channels: Union[int, Dict[str, int]], out_channels: int, metadata: Tuple[List[str], List[Tuple[str, str, str]]], heads: int = 1, **kwargs)[source]

来自“异构图变换器”论文的异构图变换器（HGT）操作符。

注意

有关使用HGT的示例，请参见examples/hetero/hgt_dblp.py。

Parameters:

in_channels (int or Dict[str, int]) – Size of each input sample of every node type, or -1 to derive the size from the first input(s) to the forward method.
out_channels (int) – Size of each output sample.
metadata (Tuple[List[str], List[Tuple[str, str, str]]]) – The metadata of the heterogeneous graph, i.e. its node and edge types given by a list of strings and a list of string triplets, respectively. See torch_geometric.data.HeteroData.metadata() for more information.
heads (int, optional) – Number of multi-head-attentions. (default: 1)
**kwargs (optional) – Additional arguments of torch_geometric.nn.conv.MessagePassing.

forward(x_dict: Dict[str, Tensor], edge_index_dict: Dict[Tuple[str, str, str], Union[Tensor, SparseTensor]]) → Dict[str, Optional[Tensor]][source]

运行模块的前向传播。

Parameters:

x_dict (Dict[str, torch.Tensor]) – 一个字典，保存每个单独节点类型的输入节点特征。
edge_index_dict (Dict[Tuple[str, str, str], torch.Tensor]) – A dictionary holding graph connectivity information for each individual edge type, either as a torch.Tensor of shape [2, num_edges] or a torch_sparse.SparseTensor.

Return type:

Dict[str, Optional[torch.Tensor]] - 每个节点类型的输出节点嵌入。如果某个节点类型没有接收到任何消息，其输出将被设置为 None。