Python 合并（加入）networkx 图
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Combine (join) networkx Graphs
提问by atomh33ls
Say I have two networkx graphs, Gand H:
假设我有两个 networkx 图，G并且H：
G=nx.Graph()
fromnodes=[0,1,1,1,1,1,2]
tonodes=[1,2,3,4,5,6,7]
for x,y in zip(fromnodes,tonodes):
G.add_edge(x,y)
H=nx.Graph()
fromnodes=range(2,8)
tonodes=range(8,14)
for x,y in zip(fromnodes,tonodes):
H.add_edge(x,y)
What is the best way to join the two networkx graphs?
加入两个networkx图的最佳方法是什么？
I'd like to preserve the node names (note the common nodes, 2 to 7). When I used nx.disjoint_union(G,H), this did not happen:
我想保留节点名称（注意常见节点，2 到 7）。当我使用时nx.disjoint_union(G,H)，这并没有发生：
>>> G.nodes()
[0, 1, 2, 3, 4, 5, 6, 7]
>>> H.nodes()
[2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13]
>>> Un= nx.disjoint_union(G,H)
>>> Un.nodes()
[0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19]
#
The Hnode labels were changed (not what I want). I want to join the graphs at the nodes with the same number.
该H节点标签被改变（不是我想要的）。我想在具有相同编号的节点处加入图形。
Note. This is not a duplicate of Combine two weighted graphs in NetworkX.
笔记。这不是在 NetworkX中结合两个加权图的副本。
采纳答案by Joel
The function you're looking for is compose, which produces a graph with all the edges and all the nodes that are in both graphs. If both graphs have a node with the same name, then a single copy ends up in the new graph. Similarly if the same edge exists in both. Here's an example, including edge/node attributes:
您正在寻找的函数是compose，它生成一个图形，其中包含两个图形中的所有边和所有节点。如果两个图都有一个同名的节点，那么新图中会出现一个副本。同样，如果两者都存在相同的边。这是一个示例，包括边/节点属性：
import networkx as nx
G=nx.Graph()
G.add_node(1, weight = 2)
G.add_node(2, weight = 3)
G.add_edge(1,2, flux = 5)
G.add_edge(2,4)
H=nx.Graph()
H.add_node(1, weight = 4)
H.add_edge(1,2, flux = 10)
H.add_edge(1,3)
F = nx.compose(G,H)
#F has all nodes & edges of both graphs, including attributes
#Where the attributes conflict, it uses the attributes of H.
G.nodes(data=True)
> NodeDataView({1: {'weight': 2}, 2: {'weight': 3}, 4: {}})
H.nodes(data=True)
> NodeDataView({1: {'weight': 4}, 2: {}, 3: {}})
F.nodes(data=True)
> NodeDataView({1: {'weight': 4}, 2: {'weight': 3}, 4: {}, 3: {}})
G.edges(data=True)
> EdgeDataView([(1, 2, {'flux': 5}), (2, 4, {})])
H.edges(data=True)
> EdgeDataView([(1, 2, {'flux': 10}), (1, 3, {})])
F.edges(data=True)
EdgeDataView([(1, 2, {'flux': 10}), (1, 3, {}), (2, 4, {})])
These preserve attributes, but obviously if there is a conflict this is not possible. The attributes of Htake precedence.
这些保留属性，但显然如果存在冲突，这是不可能的。H优先的属性。
There are also other options to do the symmetric difference, intersection, ...
还有其他选项可以进行对称差异，交集，...
If you have multiple graphs to join together, you can use compose_all, which just wraps a for loop around compose.
如果您有多个图要连接在一起，则可以使用compose_all，它只是围绕 包装了一个 for 循环compose。
回答by atomh33ls
This did it.
这做到了。
U=nx.Graph()
U.add_edges_from(G.edges()+H.edges())
U.add_nodes_from(G.nodes()+H.nodes()) #deals with isolated nodes
or, preserving the edge attributes:
或者，保留边缘属性：
U.add_edges_from(G.edges(data=True)+H.edges(data=True))
and, to also preserve the node attributes:
并且，还要保留节点属性：
U.add_nodes_from(G.nodes(data=True)+H.nodes(data=True))
