Networkx中的图论 [英] Graph Theory in Networkx

问题描述

我现在开始使用这个接口，我有一些Python的经验，但没有广泛的。我正在计算一个小图的传递性和社区结构：

  import networkx as nx 
 
 G = nx.read_edgelist（data，delimiter =' - '，nodetype = str）
 nx.transitivity（G）
 
 #find modularity 
 part = best_partition（G）
模块化（部分，G）
  

然而，我得到的传递性很好 - 有以下几点计算模块化的错误。

  NameError：name'best_partition'未定义
  

我只是遵循networkx网站提供的文档，有什么我做错了吗？

解决方案

据我所知， best_partition 不属于networkx。您好像要使用 https://sites.google.com/site/findcommunities/您可以从 https://bitbucket.org/taynaud/python-louvain/src

一旦您安装了 community ，请尝试以下代码：

 导入networkx为nx 
导入社区
导入matplotlib.pyplot为plt 
 
 G = nx.random_graphs.powerlaw_cluster_graph（ 300,1,4）
 nx.transitivity（G）
 
 #find modularity 
 part = community.best_partition（G）
 mod = community.modularity（部分，G）
 
 #plot，使用社区结构的颜色节点
 values = [part.get（node）for G.nodes（）] 
 nx.draw_spring G，cmap = plt.get_cmap（'jet'），node_color = values，node_size = 30，with_labels = False）
 plt.show（）
  

  ryan @ palms〜/ D / taynaud-python-louvain-147f09737714> pwd 
 / home / ryan / Downloads / taynaud-python-louvain-147f09737714 
 ryan @ palms〜/ D / taynaud-python-louvain-147f09737714> sudo python3 setup.py install 
  

I am starting to use this interface now, I have some experience with Python but nothing extensive. I am calculating the transitivity and community structure of a small graph:

import networkx as nx

G = nx.read_edgelist(data, delimiter='-', nodetype=str)
nx.transitivity(G)

#find modularity
part = best_partition(G)
modularity(part, G)

I get the transitivity just fine, however - there is the following error with calculating modularity.

NameError: name 'best_partition' is not defined

I just followed the documentation provided by the networkx site, is there something I am doing wrong?

解决方案

As far as I can tell best_partition isn't part of networkx. It looks like you want to use https://sites.google.com/site/findcommunities/ which you can install from https://bitbucket.org/taynaud/python-louvain/src

Once you've installed community try this code:

import networkx as nx
import community
import matplotlib.pyplot as plt

G = nx.random_graphs.powerlaw_cluster_graph(300, 1, .4)
nx.transitivity(G)

#find modularity
part = community.best_partition(G)
mod = community.modularity(part,G)

#plot, color nodes using community structure
values = [part.get(node) for node in G.nodes()]
nx.draw_spring(G, cmap = plt.get_cmap('jet'), node_color = values, node_size=30, with_labels=False)
plt.show()

edit: How I installed the community detection library

ryan@palms ~/D/taynaud-python-louvain-147f09737714> pwd
/home/ryan/Downloads/taynaud-python-louvain-147f09737714
ryan@palms ~/D/taynaud-python-louvain-147f09737714> sudo python3 setup.py install

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