dgl.remove_edges(g, eids, etype=None, store_ids=False)[source]

Remove the specified edges and return a new graph.

Also delete the features of the edges. The edges must exist in the graph. The resulting graph has the same number of the nodes as the input one, even if some nodes become isolated after the the edge removal.

  • eids (int, Tensor, iterable[int]) – The IDs of the edges to remove.

  • etype (str or (str, str, str), optional) –

    The type names of the edges. The allowed type name formats are:

    • (str, str, str) for source node type, edge type and destination node type.

    • or one str edge type name if the name can uniquely identify a triplet format in the graph.

    Can be omitted if the graph has only one type of edges.

  • store_ids (bool, optional) – If True, it will store the raw IDs of the extracted nodes and edges in the ndata and edata of the resulting graph under name dgl.NID and dgl.EID, respectively.


The graph with edges deleted.

Return type



This function preserves the batch information.


>>> import dgl
>>> import torch

Homogeneous Graphs

>>> g = dgl.graph((torch.tensor([0, 0, 2]), torch.tensor([0, 1, 2])))
>>> g.edata['he'] = torch.arange(3).float().reshape(-1, 1)
>>> g = dgl.remove_edges(g, torch.tensor([0, 1]))
>>> g
Graph(num_nodes=3, num_edges=1,
    edata_schemes={'he': Scheme(shape=(1,), dtype=torch.float32)})
>>> g.edges('all')
(tensor([2]), tensor([2]), tensor([0]))
>>> g.edata['he']

Heterogeneous Graphs

>>> 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]))
...     })
>>> g = dgl.remove_edges(g, torch.tensor([0, 1]), 'plays')
>>> g.edges('all', etype='plays')
(tensor([1, 2]), tensor([1, 1]), tensor([0, 1]))