dgl.transform.reverse¶

dgl.transform.
reverse
(g, share_ndata=False, share_edata=False)[source]¶ Return the reverse of a graph
The reverse (also called converse, transpose) of a directed graph is another directed graph on the same nodes with edges reversed in terms of direction.
Given a
DGLGraph
object, we return anotherDGLGraph
object representing its reverse.Notes
 This function does not support
BatchedDGLGraph
objects.  We do not dynamically update the topology of a graph once that of its reverse changes. This can be particularly problematic when the node/edge attrs are shared. For example, if the topology of both the original graph and its reverse get changed independently, you can get a mismatched node/edge feature.
Parameters:  g (dgl.DGLGraph) – The input graph.
 share_ndata (bool, optional) – If True, the original graph and the reversed graph share memory for node attributes. Otherwise the reversed graph will not be initialized with node attributes.
 share_edata (bool, optional) – If True, the original graph and the reversed graph share memory for edge attributes. Otherwise the reversed graph will not have edge attributes.
Examples
Create a graph to reverse.
>>> import dgl >>> import torch as th >>> g = dgl.DGLGraph() >>> g.add_nodes(3) >>> g.add_edges([0, 1, 2], [1, 2, 0]) >>> g.ndata['h'] = th.tensor([[0.], [1.], [2.]]) >>> g.edata['h'] = th.tensor([[3.], [4.], [5.]])
Reverse the graph and examine its structure.
>>> rg = g.reverse(share_ndata=True, share_edata=True) >>> print(rg) DGLGraph with 3 nodes and 3 edges. Node data: {'h': Scheme(shape=(1,), dtype=torch.float32)} Edge data: {'h': Scheme(shape=(1,), dtype=torch.float32)}
The edges are reversed now.
>>> rg.has_edges_between([1, 2, 0], [0, 1, 2]) tensor([1, 1, 1])
Reversed edges have the same feature as the original ones.
>>> g.edges[[0, 2], [1, 0]].data['h'] == rg.edges[[1, 0], [0, 2]].data['h'] tensor([[1], [1]], dtype=torch.uint8)
The node/edge features of the reversed graph share memory with the original graph, which is helpful for both forward computation and back propagation.
>>> g.ndata['h'] = g.ndata['h'] + 1 >>> rg.ndata['h'] tensor([[1.], [2.], [3.]])
 This function does not support