本文整理汇总了Python中networkx.Graph.degree_iter方法的典型用法代码示例。如果您正苦于以下问题：Python Graph.degree_iter方法的具体用法？Python Graph.degree_iter怎么用？Python Graph.degree_iter使用的例子？那么恭喜您, 这里精选的方法代码示例或许可以为您提供帮助。您也可以进一步了解该方法所在类networkx.Graph的用法示例。
在下文中一共展示了Graph.degree_iter方法的1个代码示例，这些例子默认根据受欢迎程度排序。您可以为喜欢或者感觉有用的代码点赞，您的评价将有助于系统推荐出更棒的Python代码示例。
示例1: open
# 需要导入模块: from networkx import Graph [as 别名]
# 或者: from networkx.Graph import degree_iter [as 别名]
from os import path
import numpy as np
from networkx import Graph, transitivity, clustering, average_shortest_path_length, connected_component_subgraphs
from networkx.readwrite import json_graph
if __name__ == '__main__':
with open(sys.argv[1]) as g_file:
data = json.load(g_file)
g = Graph(json_graph.node_link_graph(data))
print('Number of nodes:', g.number_of_nodes())
print('Average degree:', 2 * g.number_of_edges()/g.number_of_nodes())
print('Transitivity:', transitivity(g))
cc = clustering(g)
print('Average clustering coefficient:', np.mean(list(cc.values())))
for subgraph in connected_component_subgraphs(g):
if subgraph.number_of_nodes() > 1:
print('Average shortest path length for subgraph of', subgraph.number_of_nodes(), ':',
average_shortest_path_length(subgraph))
# Calculating average clustering coefficient for different degrees
degree_cc = {}
for node, degree in g.degree_iter():
if degree not in degree_cc:
degree_cc[degree] = []
degree_cc[degree].append(cc[node])
with open(path.join(path.dirname(sys.argv[1]), 'clustering.csv'), 'w', newline='') as cc_file:
writer = csv.DictWriter(cc_file, ['degree', 'average_cc'])
writer.writeheader()
for degree in degree_cc:
writer.writerow({'degree': degree, 'average_cc': np.mean(degree_cc[degree])})