Class AddSelfLoop

python/dgl/transforms/module.py:510–650

r"""Add self-loops for each node in the graph and return a new graph. For heterogeneous graphs, self-loops are added only for edge types with same source and destination node types. Parameters ---------- allow_duplicate : bool, optional If False, it will first remove se

class AddSelfLoop(BaseTransform):
    r"""Add self-loops for each node in the graph and return a new graph.

    For heterogeneous graphs, self-loops are added only for edge types with same
    source and destination node types.

    Parameters
    ----------
    allow_duplicate : bool, optional
        If False, it will first remove self-loops to prevent duplicate self-loops.
    new_etypes : bool, optional
        If True, it will add an edge type 'self' per node type, which holds self-loops.
    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'``.

    Example
    -------

    >>> import dgl
    >>> from dgl import AddSelfLoop

    Case1: Add self-loops for a homogeneous graph

    >>> transform = AddSelfLoop(fill_data='sum')
    >>> g = dgl.graph(([0, 0, 2], [2, 1, 0]))
    >>> g.edata['he'] = torch.arange(3).float().reshape(-1, 1)
    >>> new_g = transform(g)
    >>> print(new_g.edges())
    (tensor([0, 0, 2, 0, 1, 2]), tensor([2, 1, 0, 0, 1, 2]))
    >>> print(new_g.edata('he'))
    tensor([[0.],
            [1.],
            [2.],
            [2.],
            [1.],
            [0.]])

    Case2: Add self-loops for a heterogeneous graph

    >>> transform = AddSelfLoop(fill_data='sum')
    >>> g = dgl.heterograph({
    ...     ('user', 'follows', 'user'): (torch.tensor([1, 2]),
    ...                                   torch.tensor([0, 1])),
    ...     ('user', 'plays', 'game'): (torch.tensor([0, 1]),
    ...                                 torch.tensor([0, 1]))})
    >>> g.edata['feat'] = {('user', 'follows', 'user'): torch.randn(2, 5),
    ...                    ('user', 'plays', 'game'): torch.randn(2, 5)}
    >>> g.edata['feat1'] = {('user', 'follows', 'user'): torch.randn(2, 15),
    ...                     ('user', 'plays', 'game'): torch.randn(2, 15)}