Function remove_self_loop

python/dgl/transforms/functional.py:2048–2120

r"""Remove self-loops for each node in the graph and return a new graph. Parameters ---------- g : DGLGraph The graph. etype : str or (str, str, str), optional The type names of the edges. The allowed type name formats are: * ``(str, str, str)`` for source n

(g, etype=None)

def remove_self_loop(g, etype=None):
    r"""Remove self-loops for each node in the graph and return a new graph.

    Parameters
    ----------
    g : DGLGraph
        The graph.
    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.

    Notes
    -----
    If a node has multiple self-loops, remove them all. Do nothing for nodes without
    self-loops.

    This function preserves the batch information.

    Examples
    ---------

    >>> import dgl
    >>> import torch

    **Homogeneous Graphs**

    >>> g = dgl.graph((torch.tensor([0, 0, 0, 1]), torch.tensor([1, 0, 0, 2])))
    >>> g.edata['he'] = torch.arange(4).float().reshape(-1, 1)
    >>> g = dgl.remove_self_loop(g)
    >>> g
    Graph(num_nodes=3, num_edges=2,
        edata_schemes={'he': Scheme(shape=(2,), dtype=torch.float32)})
    >>> g.edata['he']
    tensor([[0.],[3.]])

    **Heterogeneous Graphs**

    >>> g = dgl.heterograph({
    ...     ('user', 'follows', 'user'): (torch.tensor([0, 1, 1, 1, 2]),
    ...                                   torch.tensor([0, 0, 1, 1, 1])),
    ...     ('user', 'plays', 'game'): (torch.tensor([0, 1]),
    ...                                 torch.tensor([0, 1]))
    ...     })
    >>> g = dgl.remove_self_loop(g, etype='follows')
    >>> g.num_nodes('user')
    3
    >>> g.num_nodes('game')
    2
    >>> g.num_edges('follows')
    2
    >>> g.num_edges('plays')
    2