AddSelfLoop

class dgl.transforms.AddSelfLoop(allow_duplicate=False, new_etypes=False)

Bases: dgl.transforms.module.BaseTransform

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.

Example

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

Case1: Add self-loops for a homogeneous graph

>>> transform = AddSelfLoop()
>>> g = dgl.graph(([1, 1], [1, 2]))
>>> new_g = transform(g)
>>> print(new_g.edges())
(tensor([1, 0, 1, 2]), tensor([2, 0, 1, 2]))

Case2: Add self-loops for a heterogeneous graph

>>> g = dgl.heterograph({
...     ('user', 'plays', 'game'): ([0], [1]),
...     ('user', 'follows', 'user'): ([1], [2])
... })
>>> new_g = transform(g)
>>> print(new_g.edges(etype='plays'))
(tensor([0]), tensor([1]))
>>> print(new_g.edges(etype='follows'))
(tensor([1, 0, 1, 2]), tensor([2, 0, 1, 2]))

Case3: Add self-etypes for a heterogeneous graph

>>> transform = AddSelfLoop(new_etypes=True)
>>> new_g = transform(g)
>>> print(new_g.edges(etype='follows'))
(tensor([1, 0, 1, 2]), tensor([2, 0, 1, 2]))
>>> print(new_g.edges(etype=('game', 'self', 'game')))
(tensor([0, 1]), tensor([0, 1]))