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使用dfs_tree从无向图创建有向图,但保留属性

[英]Creating a directed graph from an undirected graph with dfs_tree, but keep attributes

I want to force an undirected graph to a directed graph using a specific node as the root.我想使用特定节点作为根将无向图强制转换为有向图。 I can do this using dfs_tree() :我可以使用dfs_tree()做到这一点:

G = nx.Graph([(0, 1, {"color": "red"}), (1, 2, {"color": "blue"}), (3, 2, {"color": "green"})])
DG = nx.dfs_tree(G, 0)

But the problem is that the attributes are lost in the process:但问题是属性在这个过程中丢失了:

DG.edges(data=True)

OutEdgeDataView([(0, 1, {}), (1, 2, {}), (2, 3, {})])

Is there a different way to do this, where you don't lose the attributes?有没有不同的方法来做到这一点,你不会失去属性? Or do I have to map them back manually?还是我必须手动将它们映射回来?

If you have enough memory available, you can first create the DiGraph with all edges and then remove all, but the dfs_edges .如果你有足够的可用内存,您可以先创建DiGraph的所有边,然后删除所有,但dfs_edges This will preserve all attribute information.这将保留所有属性信息。 Alternatively, you could iterate over dfs_edges and retrieve the edge information to add the edge and the label to the directed graph.或者,您可以遍历dfs_edges并检索边信息以将边和标签添加到有向图。

import networkx as nx

G = nx.Graph([(0, 1, {"color": "red"}), (1, 2, {"color": "blue"}), (3, 2, {"color": "green"})])
DG = nx.DiGraph(G)

DG.remove_edges_from(DG.edges - nx.dfs_edges(G, 0))

print(DG.edges(data=True))
# [(0, 1, {'color': 'red'}), (1, 2, {'color': 'blue'}), (2, 3, {'color': 'green'})]

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