python igraph在fast_community.c错误：553

Question

首先，感谢阅读并可能对此作出响应。

现在的问题： 我在Python 2.7版，并且我得到试图找到使用fastgreedy算法在我的图形社区，当这个错误：

--------------------------------------------------------------------------- 
InternalError        Traceback (most recent call last) 
<ipython-input-180-3b8456851658> in <module>() 
----> 1 dendrogram = g_summary.community_fastgreedy(weights=edge_frequency.values()) 

/usr/local/lib/python2.7/site-packages/igraph/__init__.pyc in community_fastgreedy(self, weights) 
    959   in very large networks. Phys Rev E 70, 066111 (2004). 
    960   """ 
--> 961   merges, qs = GraphBase.community_fastgreedy(self, weights) 
    962 
    963   # qs may be shorter than |V|-1 if we are left with a few separated 

InternalError: Error at fast_community.c:553: fast-greedy community finding works only on graphs without multiple edges, Invalid value 

这是我建立了我的图表：

import igraph as ig 
vertices = words #about 600 words from a number of news articles: ['palestine', 'israel', 'hamas, 'nasa', 'mercury', 'water', ...] 

gen = ig.UniqueIdGenerator() 
[gen[word] for word in vertices] #generate word-to-integer mapping as each edge has to be between integer ids (words) 

edges = [] 
for ind in xrange(articles.shape[0]): # articles is a pandas dataframe; each row corresponds to an article; one column is 'top_words' which includes the top few words of each article. The above list *words* is the unique union set of top_words for all articles. 
    words_i = articles['top_words'].values[ind] # for one article, this looks like ['palestine','israel','hamas'] 
    edges.extend([(gen[x[0]],gen[x[1]]) for x in combinations(words_i,2)]) #basically there is an edge for each pair of top_words in a given article. For the example article above, we get edges between israel-palestine, israel-hamas, palestine-hamas. 

unique_edges = list(set(edges)) 
unique_edge_frequency = {} 
for e in unique_edges: 
    unique_edge_frequency[e] = edges.count(e)  

g = ig.Graph(vertex_attrs={"label": vertices}, edges=unique_edges, directed=False) 
g.es['width'] = np.asarray([unique_edge_frequency[e] for e in unique_edge_frequency.keys()])*1.0/max(unique_edge_frequency.values()) 

而这正是引发错误：

dendrogram = g.community_fastgreedy(weights=g.es['width']) 

我在做什么错了？

Answer 1

您的图包含多个边（即同一对节点之间有多个边）。快速贪婪的社区检测在这样的图表上不起作用;您必须使用g.simplify()将多个边折叠为单个边。

它似乎也正在试图根据同一对顶点之间有多少边缘来设置边缘的"width"属性。如果不建立unique_edges然后unique_edge_frequency的，你可以简单地这样做：

g = Graph(edges, directed=False) 
g.es["width"] = 1 
g.simplify(combine_edges={ "width": "sum" }) 

这只是第一次创建具有多个边的图，然后宽度为1的分配给每个边缘，终于崩溃了多条边成单同时总结它们的宽度。