mindspore_gl.HeterGraphField
- class mindspore_gl.HeterGraphField(src_idx, dst_idx, n_nodes, n_edges)[source]
The data container for a heterogeneous graph. The edge information are stored in COO format.
- Parameters
src_idx (List[Tensor]) – A list of tensor with shape \((N\_EDGES)\), with int dtype, represents the source node index of COO edge matrix.
dst_idx (List[Tensor]) – A list of tensor with shape \((N\_EDGES)\), with int dtype, represents the destination node index of COO edge matrix.
n_nodes (List[int]) – A list of integer, represent the nodes count of the graph.
n_edges (List[int]) – A list of integer, represent the edges count of the graph.
- Supported Platforms:
AscendGPU
Examples
>>> import mindspore as ms >>> from mindspore_gl import HeterGraphField >>> n_nodes = [9, 2] >>> n_edges = [11, 1] >>> src_idx = [ms.Tensor([0, 2, 2, 3, 4, 5, 5, 6, 8, 8, 8], ms.int32), ms.Tensor([0], ms.int32)] >>> dst_idx = [ms.Tensor([1, 0, 1, 5, 3, 4, 6, 4, 8, 8, 8], ms.int32), ms.Tensor([1], ms.int32)] >>> heter_graph_field = HeterGraphField(src_idx, dst_idx, n_nodes, n_edges) >>> print(heter_graph_field.get_heter_graph()) [[Tensor(shape=[11], dtype=Int32, value= [0, 2, 2, 3, 4, 5, 5, 6, 8, 8, 8]), Tensor(shape=[1], dtype=Int32, value= [0])], [Tensor(shape=[11], dtype=Int32, value= [1, 0, 1, 5, 3, 4, 6, 4, 8, 8, 8]), Tensor(shape=[1], dtype=Int32, value= [1])], [9, 2], [11, 1]]