dgl.graphbolt.fused_csc_sampling_graph

dgl.graphbolt.fused_csc_sampling_graph(csc_indptr: Tensor, indices: Tensor, node_type_offset: tensor | None = None, type_per_edge: tensor | None = None, node_type_to_id: Dict[str, int] | None = None, edge_type_to_id: Dict[str, int] | None = None, node_attributes: Dict[str, tensor] | None = None, edge_attributes: Dict[str, tensor] | None = None) → FusedCSCSamplingGraph[source]

Create a FusedCSCSamplingGraph object from a CSC representation.

Parameters:

  • csc_indptr (torch.Tensor) – Pointer to the start of each row in the indices. An integer tensor with shape (total_num_nodes+1,).

  • indices (torch.Tensor) – Column indices of the non-zero elements in the CSC graph. An integer tensor with shape (total_num_edges,).

  • node_type_offset (Optional[torch.tensor], optional) – Offset of node types in the graph, by default None.

  • type_per_edge (Optional[torch.tensor], optional) – Type ids of each edge in the graph, by default None.

  • node_type_to_id (Optional[Dict[str, int]], optional) – Map node types to ids, by default None.

  • edge_type_to_id (Optional[Dict[str, int]], optional) – Map edge types to ids, by default None.

  • node_attributes (Optional[Dict[str, torch.tensor]], optional) – Node attributes of the graph, by default None.

  • edge_attributes (Optional[Dict[str, torch.tensor]], optional) – Edge attributes of the graph, by default None.

Returns:

The created FusedCSCSamplingGraph object.

Return type:

FusedCSCSamplingGraph

Examples

>>> ntypes = {'n1': 0, 'n2': 1, 'n3': 2}
>>> etypes = {'n1:e1:n2': 0, 'n1:e2:n3': 1}
>>> csc_indptr = torch.tensor([0, 2, 5, 7, 8])
>>> indices = torch.tensor([1, 3, 0, 1, 2, 0, 3, 2])
>>> node_type_offset = torch.tensor([0, 1, 2, 4])
>>> type_per_edge = torch.tensor([0, 1, 0, 1, 1, 0, 0, 0])
>>> graph = graphbolt.fused_csc_sampling_graph(csc_indptr, indices,
...         node_type_offset=node_type_offset,
...         type_per_edge=type_per_edge,
...         node_type_to_id=ntypes, edge_type_to_id=etypes,
...         node_attributes=None, edge_attributes=None,)
>>> print(graph)
FusedCSCSamplingGraph(csc_indptr=tensor([0, 2, 5, 7, 8]),
                      indices=tensor([1, 3, 0, 1, 2, 0, 3, 2]),
                      total_num_nodes=4, num_edges={'n1:e1:n2': 5, 'n1:e2:n3': 3},
                      node_type_offset=tensor([0, 1, 2, 4]),
                      type_per_edge=tensor([0, 1, 0, 1, 1, 0, 0, 0]),
                      node_type_to_id={'n1': 0, 'n2': 1, 'n3': 2},
                      edge_type_to_id={'n1:e1:n2': 0, 'n1:e2:n3': 1},)