dgl.add_nodes

dgl.add_nodes(g, num, data=None, ntype=None)[source]

向图中添加给定数量的节点并返回一个新图。

新节点的ID将从g.num_nodes(ntype)开始。

Parameters:

num (int) – 要添加的节点数量。
data (dict[str, Tensor], optional) – 添加节点的特征数据。键是特征名称，而值是特征数据。
ntype (str, optional) – The node type name. Can be omitted if there is only one type of nodes in the graph.

Returns:

添加了新节点的图表。

Return type:

DGLGraph

注释

For features in g but not in data, DGL assigns zero features for the newly added nodes.
For feature in data but not in g, DGL assigns zero features for the existing nodes in the graph.
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.

示例

以下示例使用PyTorch后端。

>>> import dgl
>>> import torch

同构图

>>> g = dgl.graph((torch.tensor([0, 1]), torch.tensor([1, 2])))
>>> g.num_nodes()
3
>>> g = dgl.add_nodes(g, 2)
>>> g.num_nodes()
5

如果图具有一些节点特征，并且添加了没有特征的新节点，它们的特征将被填充为零。

>>> g.ndata['h'] = torch.ones(5, 1)
>>> g = dgl.add_nodes(g, 1)
>>> g.ndata['h']
tensor([[1.], [1.], [1.], [1.], [1.], [0.]])

为新节点分配特征。

>>> g = dgl.add_nodes(g, 1, {'h': torch.ones(1, 1), 'w': torch.ones(1, 1)})
>>> g.ndata['h']
tensor([[1.], [1.], [1.], [1.], [1.], [0.], [1.]])

由于data包含新的特征字段，现有节点的特征将被填充为零。

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

异构图

>>> 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.num_nodes('user')
3
>>> g = dgl.add_nodes(g, 2, ntype='user')
>>> g.num_nodes('user')
5

另请参阅

remove_nodes, add_edges, remove_edges