DGLGraph.add_edges(u, v, data=None, etype=None)

Add multiple new edges for the specified edge type

The i-th new edge will be from u[i] to v[i].

Parameters
• u (int, tensor, numpy.ndarray, list) – Source node IDs, u[i] gives the source node for the i-th new edge.

• v (int, tensor, numpy.ndarray, list) – Destination node IDs, v[i] gives the destination node for the i-th new edge.

• data (dict, optional) – Feature data of the added edges. The i-th row of the feature data corresponds to the i-th new edge.

• etype (str or tuple of str, optional) – The type of the new edges. Can be omitted if there is only one edge type in the graph.

Notes

• Inplace update is applied to the current graph.

• If end nodes of adding edges does not exists, add_nodes is invoked to add new nodes. The node features of the new nodes will be created by initializers defined with set_n_initializer() (default initializer fills zeros). In certain cases, it is recommanded to add_nodes first and then add_edges.

• If the key of data does not contain some existing feature fields, those features for the new edges will be created by initializers defined with set_n_initializer() (default initializer fills zeros).

• If the key of data contains new feature fields, those features for the old edges will be created by initializers defined with set_n_initializer() (default initializer fills zeros).

• 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 example uses PyTorch backend.

>>> import dgl
>>> import torch


Homogeneous Graphs or Heterogeneous Graphs with A Single Edge Type

>>> g = dgl.graph((torch.tensor([0, 1]), torch.tensor([1, 2])))
>>> g.num_edges()
2
>>> g.num_edges()
4


Since u or v contains a non-existing node ID, the nodes are added implicitly. >>> g.num_nodes() 4

If the graph has some edge features and new edges are added without features, their features will be created by initializers defined with set_n_initializer().

>>> g.edata['h'] = torch.ones(4, 1)
>>> g.edata['h']
tensor([[1.], [1.], [1.], [1.], [0.]])


We can also assign features for the new edges in adding new edges.

>>> g.add_edges(torch.tensor([0, 0]), torch.tensor([2, 2]),
...             {'h': torch.tensor([[1.], [2.]]), 'w': torch.ones(2, 1)})
>>> g.edata['h']
tensor([[1.], [1.], [1.], [1.], [0.], [1.], [2.]])


Since data contains new feature fields, the features for old edges will be created by initializers defined with set_n_initializer().

>>> g.edata['w']
tensor([[0.], [0.], [0.], [0.], [0.], [1.], [1.]])


Heterogeneous Graphs with Multiple Edge Types

>>> g = dgl.heterograph({
...     ('user', 'plays', 'game'): (torch.tensor([0, 1, 1, 2]),
...                                 torch.tensor([0, 0, 1, 1])),
...     ('developer', 'develops', 'game'): (torch.tensor([0, 1]),
...                                         torch.tensor([0, 1]))
...     })