Method edges

python/dgl/udf.py:146–186

Return the edges in the batch. Returns ------- (U, V, EID) : (Tensor, Tensor, Tensor) The edges in the batch. For each :math:`i`, :math:`(U[i], V[i])` is an edge from :math:`U[i]` to :math:`V[i]` with ID :math:`EID[i]`. Examples -----

(self)

        return self._edge_data

    def edges(self):
        """Return the edges in the batch.

        Returns
        -------
        (U, V, EID) : (Tensor, Tensor, Tensor)
            The edges in the batch. For each :math:`i`, :math:`(U[i], V[i])` is
            an edge from :math:`U[i]` to :math:`V[i]` with ID :math:`EID[i]`.

        Examples
        --------
        The following example uses PyTorch backend.

        >>> import dgl
        >>> import torch

        >>> # Instantiate a graph.
        >>> g = dgl.graph((torch.tensor([0, 1, 1]), torch.tensor([1, 1, 0])))

        >>> # Define a UDF that retrieves and concatenates the end nodes of the
        >>> # edges.
        >>> def edge_udf(edges):
        >>>     src, dst, _ = edges.edges()
        >>>     return {'uv': torch.stack([src, dst], dim=1).float()}

        >>> # Create a feature 'uv' with the end nodes of the edges.
        >>> g.apply_edges(edge_udf)
        >>> g.edata['uv']
        tensor([[0., 1.],
                [1., 1.],
                [1., 0.]])

        >>> # Use edge UDF in message passing.
        >>> import dgl.function as fn
        >>> g.update_all(edge_udf, fn.sum('uv', 'h'))
        >>> g.ndata['h']
        tensor([[1., 0.],
                [1., 2.]])
        """
        u, v = self._graph.find_edges(self._eid, etype=self.canonical_etype)
        return u, v, self._eid

    def batch_size(self):
        """Return the number of edges in the batch.