NetworkX - 将结果写入CSV以实现多种测量

Question

我使用NetworkX为大型网络计算4个单独的中心性度量标准，现在我想将结果写入CSV文件。我不想写对4个指标的一个CSV文件，但宁愿有类似如下：

Id, degree, between, close, eigen 

1, 0.4, 0.0, 0.5, 0.45 

2, 0.4, 0.0, 0.5, 0.45 

3, 0.6, 0.6, 0.71, 0.58 

4, 0.6, 0.7, 0.71, 0.47 

5, 0.2, 0.0, 0.45, 0.18 

6, 0.2, 0.0, 0.45, 0.18 

下面是我的代码显示什么我迄今所做的：

import networkx as nx 
G = nx.Graph() 

# add nodes and edges 
G.add_edges_from([(1,2),(1,3),(2,3),(3,4),(4,5),(4,6)]) 

# calculate centrality metrics  
degree = nx.degree_centrality(G) 
between = nx.betweenness_centrality(G) 
close = nx.closeness_centrality(G) 
eigen = nx.eigenvector_centrality(G) 

Answer 1

您可以并行迭代字典。

首先，将每个字典的(key, value)元组列表，排序上的按键：

degree = sorted(degree.items()) 
between = sorted(between.items()) 
close = sorted(close.items()) 
eigen = sorted(eigen.items()) 

接下来，创建一个并行迭代：

p = zip(degree, between, close, eigen) 

现在，p是：

[((1, 0.4), (1, 0.0), (1, 0.5), (1, 0.45698629803118)), 
((2, 0.4), (2, 0.0), (2, 0.5), (2, 0.45698629803118)), 
((3, 0.6000000000000001), 
    (3, 0.6000000000000001), 
    (3, 0.7142857142857143), 
    (3, 0.5842167062067959)), 
((4, 0.6000000000000001), 
    (4, 0.7000000000000001), 
    (4, 0.7142857142857143), 
    (4, 0.4171170012545873)), 
((5, 0.2), (5, 0.0), (5, 0.45454545454545453), (5, 0.1830727919118216)), 
((6, 0.2), (6, 0.0), (6, 0.45454545454545453), (6, 0.1830727919118216))] 

现在，遍历p的每一行：

for row in p: 
    this_keys, this_values = zip(*row) 
    assert len(set(this_keys)) == 1 # make sure we got what we expected 
    this_key = this_keys[0] 
    # Now, this_key is the key and this_values is a tuple of values 
    print this_key, ['%.2f' % v for v in this_values] 

输出是：

1 ['0.40', '0.00', '0.50', '0.46'] 
2 ['0.40', '0.00', '0.50', '0.46'] 
3 ['0.60', '0.60', '0.71', '0.58'] 
4 ['0.60', '0.70', '0.71', '0.42'] 
5 ['0.20', '0.00', '0.45', '0.18'] 
6 ['0.20', '0.00', '0.45', '0.18'] 

现在，而不是打印，可以将它们保存到一个CSV文件。

Answer 2

我发现了一个简单的答案，它将在单行上打印一个节点的多个度量的结果。

## calculate centrality metrics: 
degree = nx.degree_centrality(G) 
between = nx.betweenness_centrality(G) 
close = nx.closeness_centrality(G) 
eigen = nx.eigenvector_centrality(G) 

## print the multiple centrality metrics to a single line for each node: 
for n in G: 
    print ("%d, %f, %f, %f, %f"%(n, degree[n], between[n], close[n], eigen[n]))