AddReverseΒΆ
-
class
dgl.transforms.
AddReverse
(copy_edata=False, sym_new_etype=False)[source]ΒΆ Bases:
dgl.transforms.module.BaseTransform
Add a reverse edge \((i,j)\) for each edge \((j,i)\) in the input graph and return a new graph.
For a heterogeneous graph, it adds a βreverseβ edge type for each edge type to hold the reverse edges. For example, for a canonical edge type (βAβ, βrβ, βBβ), it adds a canonical edge type (βBβ, βrev_rβ, βAβ).
- Parameters
copy_edata (bool, optional) β If True, the features of the reverse edges will be identical to the original ones.
sym_new_etype (bool, optional) β If False, it will not add a reverse edge type if the source and destination node type in a canonical edge type are identical. Instead, it will directly add edges to the original edge type.
Example
The following example uses PyTorch backend.
>>> import dgl >>> import torch >>> from dgl import AddReverse
Case1: Add reverse edges for a homogeneous graph
>>> transform = AddReverse() >>> g = dgl.graph(([0], [1])) >>> g.edata['w'] = torch.ones(1, 2) >>> new_g = transform(g) >>> print(new_g.edges()) (tensor([0, 1]), tensor([1, 0])) >>> print(new_g.edata['w']) tensor([[1., 1.], [0., 0.]])
Case2: Add reverse edges for a homogeneous graph and copy edata
>>> transform = AddReverse(copy_edata=True) >>> new_g = transform(g) >>> print(new_g.edata['w']) tensor([[1., 1.], [1., 1.]])
Case3: Add reverse edges for a heterogeneous graph
>>> g = dgl.heterograph({ ... ('user', 'plays', 'game'): ([0, 1], [1, 1]), ... ('user', 'follows', 'user'): ([1, 2], [2, 2]) ... }) >>> new_g = transform(g) >>> print(new_g.canonical_etypes) [('game', 'rev_plays', 'user'), ('user', 'follows', 'user'), ('user', 'plays', 'game')] >>> print(new_g.edges(etype='rev_plays')) (tensor([1, 1]), tensor([0, 1])) >>> print(new_g.edges(etype='follows')) (tensor([1, 2, 2, 2]), tensor([2, 2, 1, 2]))