Function graph

python/dgl/convert.py:32–184

Create a graph and return. Parameters ---------- data : graph data The data for constructing a graph, which takes the form of :math:`(U, V)`. :math:`(U[i], V[i])` forms the edge with ID :math:`i` in the graph. The allowed data formats are: - ``(Tensor, T

(
    data,
    *,
    num_nodes=None,
    idtype=None,
    device=None,
    row_sorted=False,
    col_sorted=False,
)

def graph(
    data,
    *,
    num_nodes=None,
    idtype=None,
    device=None,
    row_sorted=False,
    col_sorted=False,
):
    """Create a graph and return.

    Parameters
    ----------
    data : graph data
        The data for constructing a graph, which takes the form of :math:`(U, V)`.
        :math:`(U[i], V[i])` forms the edge with ID :math:`i` in the graph.
        The allowed 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 of int32/int64 and 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).
    num_nodes : int, optional
        The number of nodes in the graph. If not given, this will be the largest node ID
        plus 1 from the :attr:`data` argument. If given and the value is no greater than
        the largest node ID from the :attr:`data` argument, DGL will raise an error.
    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` argument.
        See "Notes" for more details.
    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.
    row_sorted : bool, optional
        Whether or not the rows of the COO are in ascending order.
    col_sorted : bool, optional
        Whether or not the columns of the COO are in ascending order within
        each row. This only has an effect when ``row_sorted`` is True.

    Returns
    -------
    DGLGraph