Function add_self_loop

python/dgl/transforms/functional.py:1906–2042

r"""Add self-loops for each node in the graph and return a new graph. Parameters ---------- g : DGLGraph The graph. edge_feat_names : list[str], optional The names of the self-loop features to apply `fill_data`. If None, it will apply `fill_data` to all self-

(g, edge_feat_names=None, fill_data=1.0, etype=None)

def add_self_loop(g, edge_feat_names=None, fill_data=1.0, etype=None):
    r"""Add self-loops for each node in the graph and return a new graph.

    Parameters
    ----------
    g : DGLGraph
        The graph.
    edge_feat_names : list[str], optional
        The names of the self-loop features to apply `fill_data`. If None, it will apply `fill_data`
        to all self-loop features. Default: None.
    fill_data : int, float or str, optional
        The value to fill the self-loop features. Default: 1.

        * If ``fill_data`` is ``int`` or ``float``, self-loop features will be directly given by
          ``fill_data``.
        * if ``fill_data`` is ``str``, self-loop features will be generated by aggregating the
          features of the incoming edges of the corresponding nodes. The supported aggregation are:
          ``'mean'``, ``'sum'``, ``'max'``, ``'min'``.
    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.

    Return
    ------
    DGLGraph
        The graph with self-loops.

    Notes
    -----
    * The function only supports homogeneous graphs or heterogeneous graphs but
      the relation graph specified by the :attr:`etype` argument is homogeneous.
    * The function adds self-loops regardless of whether they already exist or not.
      If one wishes to have exactly one self-loop for every node,
      call :func:`remove_self_loop` before invoking :func:`add_self_loop`.
    * This function discards the batch information. Please use
      :func:`dgl.DGLGraph.set_batch_num_nodes`
      and :func:`dgl.DGLGraph.set_batch_num_edges` on the transformed graph
      to maintain the information.

    Examples
    --------
    >>> import dgl
    >>> import torch

    **Homogeneous Graphs**

    >>> g = dgl.graph((torch.tensor([0, 0, 2]), torch.tensor([2, 1, 0])))
    >>> g.ndata['hv'] = torch.arange(3).float().reshape(-1, 1)
    >>> g.edata['he'] = torch.arange(3).float().reshape(-1, 1)
    >>> g = dgl.add_self_loop(g, fill_data='sum')
    >>> g
    Graph(num_nodes=3, num_edges=6,
        ndata_schemes={'hv': Scheme(shape=(1,), dtype=torch.float32)}