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]

Bases: MessagePassing

The Heterogeneous Graph Transformer (HGT) operator from the “Heterogeneous Graph Transformer” paper.

Note

For an example of using HGT, see 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]

Runs the forward pass of the module.

Parameters:

• x_dict (Dict[str, torch.Tensor]) – A dictionary holding input node features for each individual node type.

• 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]] - The output node embeddings for each node type. In case a node type does not receive any message, its output will be set to None.

reset_parameters()[source]

Resets all learnable parameters of the module.