使用networkx绘制多级图？

Question

我试图想象一些其节点代表不同对象的图。我想创建一个看起来像这里的一个图像：

基本上，我需要一个3D图，并在同一水平上或不同级别的节点的节点间绘制边缘的能力。

Answer 1

下面的这个答案可能不是一个完整的解决方案，但是是一个使用networkx渲染3D图的工作演示。因此网络x不能渲染3D图形。我们将不得不安装mayavi来实现。

import networkx as nx 
import matplotlib.pyplot as plt 
import numpy as np 
from mayavi import mlab 

import random 

def draw_graph3d(graph, graph_colormap='winter', bgcolor = (1, 1, 1), 
       node_size=0.03, 
       edge_color=(0.8, 0.8, 0.8), edge_size=0.002, 
       text_size=0.008, text_color=(0, 0, 0)): 

    H=nx.Graph() 

    # add edges 
    for node, edges in graph.items(): 
     for edge, val in edges.items(): 
      if val == 1: 
       H.add_edge(node, edge) 

    G=nx.convert_node_labels_to_integers(H) 

    graph_pos=nx.spring_layout(G, dim=3) 

    # numpy array of x,y,z positions in sorted node order 
    xyz=np.array([graph_pos[v] for v in sorted(G)]) 

    # scalar colors 
    scalars=np.array(G.nodes())+5 
    mlab.figure(1, bgcolor=bgcolor) 
    mlab.clf() 

    #---------------------------------------------------------------------------- 
    # the x,y, and z co-ordinates are here 
    # manipulate them to obtain the desired projection perspective 
    pts = mlab.points3d(xyz[:,0], xyz[:,1], xyz[:,2], 
         scalars, 
         scale_factor=node_size, 
         scale_mode='none', 
         colormap=graph_colormap, 
         resolution=20) 
    #---------------------------------------------------------------------------- 

    for i, (x, y, z) in enumerate(xyz): 
     label = mlab.text(x, y, str(i), z=z, 
          width=text_size, name=str(i), color=text_color) 
     label.property.shadow = True 

    pts.mlab_source.dataset.lines = np.array(G.edges()) 
    tube = mlab.pipeline.tube(pts, tube_radius=edge_size) 
    mlab.pipeline.surface(tube, color=edge_color) 

    mlab.show() # interactive window 

# create tangled hypercube 
def make_graph(nodes): 

    def make_link(graph, i1, i2): 
     graph[i1][i2] = 1 
     graph[i2][i1] = 1 

    n = len(nodes) 

    if n == 1: return {nodes[0]:{}} 

    nodes1 = nodes[0:n/2] 
    nodes2 = nodes[n/2:] 
    G1 = make_graph(nodes1) 
    G2 = make_graph(nodes2) 

    # merge G1 and G2 into a single graph 
    G = dict(G1.items() + G2.items()) 

    # link G1 and G2 
    random.shuffle(nodes1) 
    random.shuffle(nodes2) 
    for i in range(len(nodes1)): 
     make_link(G, nodes1[i], nodes2[i]) 

    return G 

# graph example 
nodes = range(10) 
graph = make_graph(nodes) 
draw_graph3d(graph) 

此代码已从示例here之一修改。 当你成功实现目标时，请在这种情况下发布代码。