【dgl学习】dgl处理图中的节点/边的属性/特征/类型

1 dgl.DGLGraph.nodes

  函数的主要功能有两个

• 返回图中单个节点类型的ID
• 获取或者设置单个节点类型的特征

主要有两个应用同构图和异构图：

1.1 举个例子

import dgl
import torch

# 定义同构图
g = dgl.graph((torch.tensor([0, 1]), torch.tensor([1, 2])))
# 定义异构图
hg = dgl.heterograph({
    ('user', 'follows', 'user'): (torch.tensor([0, 1]), torch.tensor([1, 2])),
    ('user', 'plays', 'game'): (torch.tensor([3, 4]), torch.tensor([5, 6]))
})

# 获取节点ID
g.nodes()
# tensor([0, 1, 2])

hg.nodes('user')
# tensor([0, 1, 2, 3, 4])

++++++++++++++++++++++
# 设置获取节点特征
hg.nodes['user'].data['h'] = torch.ones(5, 1)
hg.nodes['user'].data['h']
# tensor([[1.], [1.], [1.], [1.], [1.]])
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3 dgl.DGLGraph.edges

见参考文献2

dgl.DGLGraph.edata

函数功能：

• Return an edge data view for setting/getting edge features.（返回获取或者设置边的特征，下面是两种不同类型图的获取方式）

• Let g be a DGLGraph. If g is a graph of a single edge type, g.edata[feat] returns the edge feature associated with the name feat.通过g.edata[feat] 返回一个样例。

• If g is a graph of multiple edge types, g.edata[feat] returns a dict[str, Tensor] mapping canonical edge types to the edge features associated with the name feat for the corresponding type. 通过g.edata[feat] 返回一个字典。

• Notes : For setting features, the device of the features must be the same as the device of the graph.

举个例子

import dgl
import torch

# Set and get feature ‘h’ for a graph of a single edge type.
g = dgl.graph((torch.tensor([0, 1]), torch.tensor([1, 2])))
g.edata['h'] = torch.ones(2, 1)
g.edata['h']

# Set and get feature ‘h’ for a graph of multiple edge types.
g = dgl.heterograph({
    ('user', 'follows', 'user'): (torch.tensor([1, 2]), torch.tensor([3, 4])),
    ('user', 'plays', 'user'): (torch.tensor([2, 2]), torch.tensor([1, 1])),
    ('player', 'plays', 'game'): (torch.tensor([2, 2]), torch.tensor([1, 1]))
})
g.edata['h'] = {('user', 'follows', 'user'): torch.zeros(2, 1),
                ('user', 'plays', 'user'): torch.ones(2, 1)}
g.edata['h']
# {('user', 'follows', 'user'): tensor([[0.], [0.]]),
# ('user', 'plays', 'user'): tensor([[1.], [1.]])}
 
g.edata['h'] = {('user', 'follows', 'user'): torch.ones(2, 1)}
g.edata['h']

# {('user', 'follows', 'user'): tensor([[1.], [1.]]),
# ('user', 'plays', 'user'): tensor([[1.], [1.]])}
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3 参考文献

[1]dgl.DGLGraph.node
[2]dgl.DGLGraph.edges
[3]dgl.DGLGraph.edata