1 load_data = sio.loadmat(load_path) 2 3 #阈值处理 4 mat=np.array(load_data['R']) 5 mat[mat<0]=0 6 mat[mat<0.45]=0 7 8 #写入txt文件 9 # np.savetxt(thr_path, mat); 10 11 #计算degree 12 g = nx.Graph() 13 for i in range(len(mat)): 14 for j in range(len(mat)): 15 if(mat[i,j]>0): #删除边的权重小于等于0的边 16 g.add_edge(i, j,weight=mat[i,j]) 17 18 # nx.draw(g) 19 # nx.draw(g,with_labels=True) # 将标号标出来 20 # plt.show() 21 22 # 按权重划分为重权值得边和轻权值的边 23 elarge = [(u, v) for (u, v, d) in g.edges(data=True) if d['weight'] > 0.5] 24 esmall = [(u, v) for (u, v, d) in g.edges(data=True) if d['weight'] <= 0.5] 25 # 节点位置 26 pos = nx.spring_layout(g) # positions for all nodes 27 # 首先画出节点位置 28 # nodes 29 nx.draw_networkx_nodes(g, pos, node_color='b',node_size=300,alpha=0.4) 30 # 根据权重，实线为权值大的边，虚线为权值小的边 31 # edges 32 nx.draw_networkx_edges(g, pos, edgelist=elarge, 33 width=3) 34 nx.draw_networkx_edges(g, pos, edgelist=esmall, 35 width=1, alpha=0.5, edge_color='g', style='dashed') 36 37 # labels标签定义 38 nx.draw_networkx_labels(g, pos, font_size=13, font_family='sans-serif') 39 40 plt.axis('off') 41 plt.savefig("weighted_graph.png") # save as png 42 plt.show()
Result:
局部放大：
