dgl.remove_nodes¶

dgl.remove_nodes(g, nids, ntype=None, store_ids=False)[source]¶

Remove the specified nodes and return a new graph.

Also delete the features. Edges that connect from/to the nodes will be removed as well. After the removal, DGL re-labels the remaining nodes and edges with IDs from 0.

Parameters

nids (int, Tensor, iterable[int]) – The nodes to be removed.
ntype (str, optional) – The type of the nodes to remove. Can be omitted if there is only one node type in the graph.
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.

Returns

The graph with nodes deleted.

Return type

DGLGraph

Notes

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

>>> import dgl
>>> import torch

Homogeneous Graphs

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

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_nodes(g, torch.tensor([0, 1]), ntype='game')
>>> g.num_nodes('user')
3
>>> g.num_nodes('game')
0
>>> g.num_edges('plays')
0