NetworkX-根据权重画图

  load_data = sio.loadmat(load_path)

     #阈值处理
       mat=np.array(load_data['R'])
       mat[mat<0]=0
       mat[mat<0.45]=0

     #写入txt文件
      # np.savetxt(thr_path, mat);

       #计算degree
       g = nx.Graph()
       for i in range(len(mat)):
           for j in range(len(mat)):
               if(mat[i,j]>0):  #删除边的权重小于等于0的边
                 g.add_edge(i, j,weight=mat[i,j])

       # nx.draw(g)
       # nx.draw(g,with_labels=True)  # 将标号标出来
       # plt.show()

       # 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_color='b',node_size=300,alpha=0.4)
       # 根据权重，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=3)
       nx.draw_networkx_edges(g, pos, edgelist=esmall,
                              width=1, alpha=0.5, edge_color='g', style='dashed')

       # labels标签定义
       nx.draw_networkx_labels(g, pos, font_size=13, font_family='sans-serif')

       plt.axis('off')
       plt.savefig("weighted_graph.png")  # save as png
       plt.show()

Result:

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