Method adj_external

python/dgl/heterograph.py:3838–3908

Return the adjacency matrix in an external format, such as Scipy or backend dependent sparse tensor. By default, a row of returned adjacency matrix represents the source of an edge and the column represents the destination. When transpose is True, a row represents t

(
        self, transpose=False, ctx=F.cpu(), scipy_fmt=None, etype=None
    )

        )

    def adj_external(
        self, transpose=False, ctx=F.cpu(), scipy_fmt=None, etype=None
    ):
        """Return the adjacency matrix in an external format, such as Scipy or
        backend dependent sparse tensor.

        By default, a row of returned adjacency matrix represents the
        source of an edge and the column represents the destination.

        When transpose is True, a row represents the destination and a column
        represents the source.

        Parameters
        ----------
        transpose : bool, optional
            A flag to transpose the returned adjacency matrix. (Default: False)
        ctx : context, optional
            The context of returned adjacency matrix. (Default: cpu)
        scipy_fmt : str, optional
            If specified, return a scipy sparse matrix in the given format.
            Otherwise, return a backend dependent sparse tensor. (Default: None)
        etype : str or (str, str, str), optional
            The type names of the edges. The allowed type name formats are:

            * ``(str, str, str)`` for source node type, edge type and destination node type.
            * or one ``str`` edge type name if the name can uniquely identify a
              triplet format in the graph.

            Can be omitted if the graph has only one type of edges.

        Returns
        -------
        SparseTensor or scipy.sparse.spmatrix
            Adjacency matrix.

        Examples
        --------

        The following example uses PyTorch backend.

        >>> import dgl
        >>> import torch

        Instantiate a heterogeneous graph.

        >>> g = dgl.heterograph({
        ...     ('user', 'follows', 'user'): ([0, 1], [0, 1]),
        ...     ('developer', 'develops', 'game'): ([0, 1], [0, 2])
        ... })

        Get a backend dependent sparse tensor. Here we use PyTorch for example.

        >>> g.adj_external(etype='develops')
        tensor(indices=tensor([[0, 1],
                               [0, 2]]),
               values=tensor([1., 1.]),
               size=(2, 3), nnz=2, layout=torch.sparse_coo)