您处在正确的轨道上；可以通过communities.optimal_count检索社区的***数量(其中最优"被定义为最大化模块化得分的社区数量")，并且可以使用communities.as_clustering(num_communities)将社区结构转换为平坦的不连续聚类.如果恰好等于communities.optimal_count，则可以忽略社区数目.完成后，您将获得一个具有membership属性的VertexClustering对象，该属性为您提供图形中每个顶点的聚簇索引

You are on the right track; the optimal number of communities (where "optimal" is defined as "the number of communities that maximizes the modularity score) can be retrieved by communities.optimal_count and the community structure can be converted into a flat disjoint clustering using communities.as_clustering(num_communities). Actually, the number of communities can be omitted if it happens to be equal to communities.optimal_count. Once you've done that, you get a VertexClustering object with a membership property which gives you the cluster index for each vertex in the graph.

为清楚起见，我将您的communities变量重命名为dendrogram，因为边缘中间性社区检测算法实际上会生成树状图::

For sake of clarity, I'm renaming your communities variable to dendrogram because the edge betweenness community detection algorithm actually produces a dendrogram::

# calculate dendrogram
dendrogram = graph.community_edge_betweenness()
# convert it into a flat clustering
clusters = dendrogram.as_clustering()
# get the membership vector
membership = clusters.membership

现在，我们可以开始将成员矢量和节点名称一起写入CSV文件:

Now we can start writing the membership vector along with the node names into a CSV file::

import csv
from itertools import izip

writer = csv.writer(open("output.csv", "wb"))
for name, membership in izip(graph.vs["name"], membership):
    writer.writerow([name, membership])

如果您使用的是Python 3，请使用zip而不是izip，并且无需导入itertools.

If you are using Python 3, use zip instead of izip and there is no need to import itertools.