dgl.node_subgraph¶

dgl.node_subgraph(graph, nodes, *, relabel_nodes=True, store_ids=True, output_device=None)[source]¶

Return a subgraph induced on the given nodes.

A node-induced subgraph is a graph with edges whose endpoints are both in the specified node set. In addition to extracting the subgraph, DGL also copies the features of the extracted nodes and edges to the resulting graph. The copy is lazy and incurs data movement only when needed.

If the graph is heterogeneous, DGL extracts a subgraph per relation and composes them as the resulting graph. Thus, the resulting graph has the same set of relations as the input one.

Parameters

graph (DGLGraph) – The graph to extract subgraphs from.
nodes (nodes or dict[str, nodes]) –
The nodes to form the subgraph. The allowed nodes formats are:
- Int Tensor: Each element is a node ID. The tensor must have the same device type and ID data type as the graph’s.
- iterable[int]: Each element is a node ID.
- Bool Tensor: Each \(i^{th}\) element is a bool flag indicating whether node \(i\) is in the subgraph.
If the graph is homogeneous, one can directly pass the above formats. Otherwise, the argument must be a dictionary with keys being node types and values being the node IDs in the above formats.
relabel_nodes (bool, optional) – If True, the extracted subgraph will only have the nodes in the specified node set and it will relabel the nodes in order.
store_ids (bool, optional) – If True, it will store the raw IDs of the extracted edges in the edata of the resulting graph under name dgl.EID; if relabel_nodes is True, it will also store the raw IDs of the specified nodes in the ndata of the resulting graph under name dgl.NID.
output_device (Framework-specific device context object, optional) – The output device. Default is the same as the input graph.

Returns

G – The subgraph.

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

The following example uses PyTorch backend.

>>> import dgl
>>> import torch

Extract a subgraph from a homogeneous graph.

>>> g = dgl.graph(([0, 1, 2, 3, 4], [1, 2, 3, 4, 0]))  # 5-node cycle
>>> sg = dgl.node_subgraph(g, [0, 1, 4])
>>> sg
Graph(num_nodes=3, num_edges=2,
      ndata_schemes={'_ID': Scheme(shape=(), dtype=torch.int64)}
      edata_schemes={'_ID': Scheme(shape=(), dtype=torch.int64)})
>>> sg.edges()
(tensor([0, 2]), tensor([1, 0]))
>>> sg.ndata[dgl.NID]  # original node IDs
tensor([0, 1, 4])
>>> sg.edata[dgl.EID]  # original edge IDs
tensor([0, 4])

Specify nodes using a boolean mask.

>>> nodes = torch.tensor([True, True, False, False, True])  # choose nodes [0, 1, 4]
>>> dgl.node_subgraph(g, nodes)
Graph(num_nodes=3, num_edges=2,
      ndata_schemes={'_ID': Scheme(shape=(), dtype=torch.int64)}
      edata_schemes={'_ID': Scheme(shape=(), dtype=torch.int64)})

The resulting subgraph also copies features from the parent graph.

>>> g.ndata['x'] = torch.arange(10).view(5, 2)
>>> sg = dgl.node_subgraph(g, [0, 1, 4])
>>> sg
Graph(num_nodes=3, num_edges=2,
      ndata_schemes={'x': Scheme(shape=(2,), dtype=torch.int64),
                     '_ID': Scheme(shape=(), dtype=torch.int64)}
      edata_schemes={'_ID': Scheme(shape=(), dtype=torch.int64)})
>>> sg.ndata['x']
tensor([[0, 1],
        [2, 3],
        [8, 9]])

Extract a subgraph from a hetergeneous graph.

>>> g = dgl.heterograph({
>>>     ('user', 'plays', 'game'): ([0, 1, 1, 2], [0, 0, 2, 1]),
>>>     ('user', 'follows', 'user'): ([0, 1, 1], [1, 2, 2])
>>> })
>>> sub_g = dgl.node_subgraph(g, {'user': [1, 2]})
>>> sub_g
Graph(num_nodes={'game': 0, 'user': 2},
      num_edges={('user', 'follows', 'user'): 2, ('user', 'plays', 'game'): 0},
      metagraph=[('user', 'user', 'follows'), ('user', 'game', 'plays')])