# dgl.to_bidirected¶

dgl.to_bidirected(g, copy_ndata=False, readonly=None)[source]

Convert the graph to a bi-directional simple graph and return.

For an input graph $$G$$, return a new graph $$G'$$ such that an edge $$(u, v)\in G'$$ if and only if there exists an edge $$(u, v)\in G$$ or an edge $$(v, u)\in G$$. The resulting graph $$G'$$ is a simple graph, meaning there is no parallel edge.

The operation only works for edges whose two endpoints belong to the same node type. DGL will raise error if the input graph is heterogeneous and contains edges with different types of endpoints.

Parameters
• g (DGLGraph) – The input graph.

• copy_ndata (bool, optional) – If True, the node features of the bidirected graph are copied from the original graph. If False, the bidirected graph will not have any node features. (Default: False)

Returns

The bidirected graph

Return type

DGLGraph

Notes

If copy_ndata is True, the resulting graph will share the node feature tensors with the input graph. Hence, users should try to avoid in-place operations which will be visible to both graphs.

This function discards the batch information. Please use dgl.DGLGraph.set_batch_num_nodes() and dgl.DGLGraph.set_batch_num_edges() on the transformed graph to maintain the information.

Examples

The following examples use PyTorch backend.

>>> import dgl
>>> import torch as th
>>> g = dgl.graph((th.tensor([0, 1, 2]), th.tensor([1, 2, 0])))
>>> bg1 = dgl.to_bidirected(g)
>>> bg1.edges()
(tensor([0, 1, 2, 1, 2, 0]), tensor([1, 2, 0, 0, 1, 2]))


The graph already have i->j and j->i

>>> g = dgl.graph((th.tensor([0, 1, 2, 0]), th.tensor([1, 2, 0, 2])))
>>> bg1 = dgl.to_bidirected(g)
>>> bg1.edges()
(tensor([0, 1, 2, 1, 2, 0]), tensor([1, 2, 0, 0, 1, 2]))


Heterogeneous graphs with Multiple Edge Types

>>> g = dgl.heterograph({
...     ('user', 'wins', 'user'): (th.tensor([0, 2, 0, 2]), th.tensor([1, 1, 2, 0])),
...     ('user', 'follows', 'user'): (th.tensor([1, 2, 1]), th.tensor([2, 1, 1]))
... })
>>> bg1 = dgl.to_bidirected(g)
>>> bg1.edges(etype='wins')
(tensor([0, 0, 1, 1, 2, 2]), tensor([1, 2, 0, 2, 0, 1]))
>>> bg1.edges(etype='follows')
(tensor([1, 1, 2]), tensor([1, 2, 1]))