Function create_block

python/dgl/convert.py:389–623

Create a message flow graph (MFG) as a :class:`DGLBlock` object. Parameters ---------- data_dict : graph data The dictionary data for constructing a MFG. The keys are in the form of string triplets (src_type, edge_type, dst_type), specifying the source node type,

(
    data_dict,
    num_src_nodes=None,
    num_dst_nodes=None,
    idtype=None,
    device=None,
    node_count_check=True,
)

def create_block(
    data_dict,
    num_src_nodes=None,
    num_dst_nodes=None,
    idtype=None,
    device=None,
    node_count_check=True,
):
    """Create a message flow graph (MFG) as a :class:`DGLBlock` object.

    Parameters
    ----------
    data_dict : graph data
        The dictionary data for constructing a MFG. The keys are in the form of
        string triplets (src_type, edge_type, dst_type), specifying the source node type,
        edge type, and destination node type. The values are graph data in the form of
        :math:`(U, V)`, where :math:`(U[i], V[i])` forms the edge with ID :math:`i`.
        The allowed graph data formats are:

        - ``(Tensor, Tensor)``: Each tensor must be a 1D tensor containing node IDs. DGL calls
          this format "tuple of node-tensors". The tensors should have the same data type,
          which must be either int32 or int64. They should also have the same device context
          (see below the descriptions of :attr:`idtype` and :attr:`device`).
        - ``('coo', (Tensor, Tensor))``: Same as ``(Tensor, Tensor)``.
        - ``('csr', (Tensor, Tensor, Tensor))``: The three tensors form the CSR representation
          of the graph's adjacency matrix.  The first one is the row index pointer.  The
          second one is the column indices.  The third one is the edge IDs, which can be empty
          to represent consecutive integer IDs starting from 0.
        - ``('csc', (Tensor, Tensor, Tensor))``: The three tensors form the CSC representation
          of the graph's adjacency matrix.  The first one is the column index pointer.  The
          second one is the row indices.  The third one is the edge IDs, which can be empty
          to represent consecutive integer IDs starting from 0.

        The tensors can be replaced with any iterable of integers (e.g. list, tuple,
        numpy.ndarray).

        If you would like to create a MFG with a single source node type, a single destination
        node type, and a single edge type, then you can pass in the graph data directly
        without wrapping it as a dictionary.
    num_src_nodes : dict[str, int] or int, optional
        The number of nodes for each source node type, which is a dictionary mapping a node type
        :math:`T` to the number of :math:`T`-typed source nodes.

        If not given for a node type :math:`T`, DGL finds the largest ID appearing in *every*
        graph data whose source node type is :math:`T`, and sets the number of nodes to
        be that ID plus one. If given and the value is no greater than the largest ID for some
        source node type, DGL will raise an error. By default, DGL infers the number of nodes for
        all source node types.

        If you would like to create a MFG with a single source node type, a single destination
        node type, and a single edge type, then you can pass in an integer to directly
        represent the number of source nodes.
    num_dst_nodes : dict[str, int] or int, optional
        The number of nodes for each destination node type, which is a dictionary mapping a node
        type :math:`T` to the number of :math:`T`-typed destination nodes.

        If not given for a node type :math:`T`, DGL finds the largest ID appearing in *every*
        graph data whose destination node type is :math:`T`, and sets the number of nodes to