import networkx as nx # 创建一个大型无向图 G = nx.Graph() G.add_edges_from([(i, i+1) for i in range(10**6)]) # 使用生成器逐个处理连通分量 for component in nx.connected_components(G): print("Connected component:", component) ...
largest_cc = max(nx.connected_components(G), key=len)#获取最大的连通子图 参考资料 【1】http://networkx.github.io/documentation/networkx-1.9/reference/api_1.9.html#miscellaneous-changes 【2】http://stackoverflow.com/questions/26105764/how-do-i-get-the-giant-component-of-a-networkx-graph/261058...
strongly_connected_components(G)) # 打印结果 for component in scc: print(component) 在上述示例中,我们首先创建了一个有向图,并添加了一些节点和边。然后,我们使用strongly_connected_components函数计算了强连接组件,并将结果打印出来。 腾讯云提供了一系列与图相关的产品和服务,例如腾讯云图数据库 Neptune,它是...
43 # identify largest connected component44 Gcc = sorted(nx.connected_component_subgraphs(G), key=len, reverse=True)45 G0 = Gcc[0]46 nx.draw_networkx_edges(G0, pos,47 with_labels=False,48 edge_color='r',49 width=6.050 )51 # show other connected components52 for Gi in Gcc[1:]:53...
图标Graph 本文参考: https://networkx.github.io/documentation/stable/auto_examples/index.html 1. 基础Basic 读写图 Read and write graphs 属性Properties ## 读写图 Read and write graphs# Author: Aric Hagberg (hagberg@lanl.gov)# Copyright (C) 2004-2019 by# Aric Hagberg <hagberg@lanl.gov># Da...
(largest_hub, degree) = sorted(node_and_degree, key=itemgetter(1))[-1] # Create ego graph of main hub hub_ego = nx.ego_graph(G, largest_hub) # Draw graph pos = nx.spring_layout(hub_ego) nx.draw(hub_ego, pos, node_color='b', node_size=50, with_labels=False) ...
subgraphs = [gforginnx.connected_component_subgraphs(G.to_undirected())] subgraphs= sorted(subgraphs, key=lambdax: x.number_of_nodes(), reverse=True)print([s.number_of_nodes()forsinsubgraphs[:10]]) 把所有的子网络按照节点数从大到小排列,我们发现90%的节点组成了一个大网络,其他的网络节点就要...
#use Networkx to load a Noded shapefile#returns a graph where each node is a coordinate pair#and the edge is the line connecting the two nodes#A graph is not always connected, so we take the largest connected subgraph#by using the connected_component_subgraphs function.#获取连接子图#获取...
# 需要导入模块: import networkx [as 别名]# 或者: from networkx importstrongly_connected_components[as 别名]deftest_condensation_as_quotient(self):"""This tests that the condensation of a graph can be viewed as the quotient graph under the "in the same connected component" equivalence ...
"""g = networkx.read_gml(infile) m = networkx.betweenness_centrality(g) l = sorted(m.items(), key = operator.itemgetter(1), reverse =True) largest_component = max(networkx.connected_components(g), key = len) n = len(g.nodes())foriinrange(...