pylibcugraphops.pytorch.operators.agg_hg_basis_n2n_post#
- pylibcugraphops.pytorch.operators.agg_hg_basis_n2n_post(feat: Tensor, weights_comb: Optional[Tensor], graph: HeteroCSC, concat_own: bool = False, norm_by_out_degree: bool = False) Tensor#
PyTorch autograd function for node-to-node RGCN-like basis regularized aggregation, with features being transformed after (post) this aggregation.
- Parameters:
- feattorch.Tensor
The input node features. Shape:
(n_src_nodes, dim_in).- weights_combOptional[torch.Tensor], default=None
The combination weights. If None, no weights are used and features of different edge types are aggregated into separate output dimensions. Shape:
(n_edge_types, n_bases)if provided.- graphHeteroCSC
The graph used for the operation.
- concat_ownbool, default=False
Concatenate output node embeddings in the aggregation.
- norm_by_out_degreebool, default=False
If set, output embeddings are normed by the degree of the output node per edge type.
- Returns:
- outputtorch.Tensor
The aggregation output. Shape for
concat_own=False:(n_dst_nodes, dim_out). Shape forconcat_own=True:(n_dst_nodes, dim_out + dim_in), wheredim_out = dim_in * n_basesifweights_combis provided, anddim_out = dim_in * n_edge_typesif not provided.