首先输入边和边的权重,随后画出节点位置。依据权重大小划分实边和虚边
#coding:utf-8 #!/usr/bin/env python """ An example using Graph as a weighted network. """ __author__ = """Aric Hagberg (hagberg@lanl.gov)""" try: import matplotlib.pyplot as plt except: raise import networkx as nx G=nx.Graph() #加入带权边 G.add_edge('a','b',weight=0.6) G.add_edge('a','c',weight=0.2) G.add_edge('c','d',weight=0.1) G.add_edge('c','e',weight=0.7) G.add_edge('c','f',weight=0.9) G.add_edge('a','d',weight=0.3) #按权重划分为重权值得边和轻权值的边 elarge=[(u,v) for (u,v,d) in G.edges(data=True) if d['weight'] >0.5] esmall=[(u,v) for (u,v,d) in G.edges(data=True) if d['weight'] <=0.5] #节点位置 pos=nx.spring_layout(G) # positions for all nodes #首先画出节点位置 # nodes nx.draw_networkx_nodes(G,pos,node_size=700) #依据权重,实线为权值大的边,虚线为权值小的边 # edges nx.draw_networkx_edges(G,pos,edgelist=elarge, width=6) nx.draw_networkx_edges(G,pos,edgelist=esmall, width=6,alpha=0.5,edge_color='b',style='dashed') # labels标签定义 nx.draw_networkx_labels(G,pos,font_size=20,font_family='sans-serif') plt.axis('off') plt.savefig("weighted_graph.png") # save as png plt.show() # display