Function heterograph

python/dgl/convert.py:208–386

Create a heterogeneous graph and return. Parameters ---------- data_dict : graph data The dictionary data for constructing a heterogeneous graph. The keys are in the form of string triplets (src_type, edge_type, dst_type), specifying the source node, edge, and de

(data_dict, num_nodes_dict=None, idtype=None, device=None)

def heterograph(data_dict, num_nodes_dict=None, idtype=None, device=None):
    """Create a heterogeneous graph and return.

    Parameters
    ----------
    data_dict : graph data
        The dictionary data for constructing a heterogeneous graph. The keys are in the form of
        string triplets (src_type, edge_type, dst_type), specifying the source node,
        edge, and destination node types. 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
          (i.e. with 0 elements) 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).
    num_nodes_dict : dict[str, int], optional
        The number of nodes for some node types, which is a dictionary mapping a node type
        :math:`T` to the number of :math:`T`-typed nodes. If not given for a node type
        :math:`T`, DGL finds the largest ID appearing in *every* graph data whose source
        or destination 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 node type,
        DGL will raise an error. By default, DGL infers the number of nodes for all node types.
    idtype : int32 or int64, optional
        The data type for storing the structure-related graph information such as node and
        edge IDs. It should be a framework-specific data type object (e.g., ``torch.int32``).
        If ``None`` (default), DGL infers the ID type from the :attr:`data_dict` argument.
    device : device context, optional
        The device of the returned graph, which should be a framework-specific device object
        (e.g., ``torch.device``). If ``None`` (default), DGL uses the device of the tensors of
        the :attr:`data` argument. If :attr:`data` is not a tuple of node-tensors, the
        returned graph is on CPU.  If the specified :attr:`device` differs from that of the
        provided tensors, it casts the given tensors to the specified device first.

    Returns
    -------
    DGLGraph
        The created graph.

    Notes
    -----
    1. If the :attr:`idtype` argument is not given then:

       - in the case of the tuple of node-tensor format, DGL uses
         the data type of the given ID tensors.
       - in the case of the tuple of sequence format, DGL uses int64.