- class CuGraphRGCNConv(in_channels: int, out_channels: int, num_relations: int, num_bases: Optional[int] = None, aggr: str = 'mean', root_weight: bool = True, bias: bool = True)
The relational graph convolutional operator from the “Modeling Relational Data with Graph Convolutional Networks” paper.
- forward(x: Tensor, csc: Tuple[Tensor, Tensor, int], edge_type: Tensor, max_num_neighbors: Optional[int] = None) Tensor
Runs the forward pass of the module.
x (torch.Tensor) – The node features.
csc ((torch.Tensor, torch.Tensor)) – A tuple containing the CSC representation of a graph, given as a tuple of
(row, colptr). Use the
to_csc()method to convert an
edge_indexrepresentation to the desired format.
edge_type (torch.Tensor) – The edge type.
max_num_neighbors (int, optional) – The maximum number of neighbors of a target node. It is only effective when operating in a bipartite graph.. When not given, the value will be computed on-the-fly, leading to slightly worse performance. (default:
Resets all learnable parameters of the module.