之前记录过基于R语言和Octave的多层网络可视化工具，但安装起来比较麻烦，发现Github上有基于Python的版本，提供了supra-adjency和多关系网络可视化，直接看：https://github.com/nkoub/multinetx

据说，安装比较简单：

pip install multinetx

我安装完了之后是这样的：

multinetx==1.0.dev0

然后美滋滋引用一下demo的代码：

##Import standard libraries

import numpy as np
import matplotlib.pyplot as plt

##Import the package MultiNetX

import multinetx as mx

##Create three Erd"os- R'enyi networks with N nodes for each layer

N = 50
g1 = mx.erdos_renyi_graph(N,0.07,seed=218)
g2 = mx.erdos_renyi_graph(N,0.07,seed=211)
g3 = mx.erdos_renyi_graph(N,0.07,seed=208)

##Define the type of interconnection between the layers

adj_block = mx.lil_matrix(np.zeros((N*3,N*3)))

adj_block[0:  N,  N:2*N] = np.identity(N)    # L_12
adj_block[0:  N,2*N:3*N] = np.identity(N)    # L_13
#adj_block[N:2*N,2*N:3*N] = np.identity(N)    # L_23
adj_block += adj_block.T

##Create an instance of the MultilayerGraph class

mg = mx.MultilayerGraph(list_of_layers=[g1,g2,g3],
                        inter_adjacency_matrix=adj_block)

mg.set_edges_weights(inter_layer_edges_weight=4)

mg.set_intra_edges_weights(layer=0,weight=1)
mg.set_intra_edges_weights(layer=1,weight=2)
mg.set_intra_edges_weights(layer=2,weight=3)

##Plot the adjacency matrix and the multiplex networks

fig = plt.figure(figsize=(15,5))
ax1 = fig.add_subplot(121)
ax1.imshow(mx.adjacency_matrix(mg,weight='weight').todense(),
          origin='upper',interpolation='nearest',cmap=plt.cm.jet_r)
ax1.set_title('supra adjacency matrix')

ax2 = fig.add_subplot(122)
ax2.axis('off')
ax2.set_title('regular interconnected network')
pos = mx.get_position(mg,
                      mx.fruchterman_reingold_layout(mg.get_layer(0)),
                      layer_vertical_shift=1.4,
                      layer_horizontal_shift=0.0,
                      proj_angle=7)
mx.draw_networkx(mg,pos=pos,ax=ax2,node_size=50,with_labels=False,
                 edge_color=[mg[a][b]['weight'] for a,b in mg.edges()],
                 edge_cmap=plt.cm.jet_r)
plt.show()

报错。

pip 安装的那么顺利，竟然报错。。。

PS C:\Users\Administrator\Documents\Paper\Algorithms> python run.py
Traceback (most recent call last):
  File "run.py", line 168, in <module>
    testMultinetx()
  File "run.py", line 113, in testMultinetx
    g1 = mx.erdos_renyi_graph(N,0.07,seed=218)
AttributeError: module 'multinetx' has no attribute 'erdos_renyi_graph'

说好的小图图呢？无奈之下跳进去看看multinetx的__init__.py发现是空的。

看github上的资源也是空的，可能作者提交新版本的时候忘记了吧。。。

好在作者给出了1.0beta版的源码，下载下来，解压到python的库里，比如我的路径是这样的：

D:\Python36\Lib\site-packages\multinetx

开始运行，各种小坑。可能是我的visual studio code没有那么智能，总也不能处理好tab和空格。

看在它小巧轻便的份上原谅它了。

手动调一下几个文件，最终把调整好的给大家传一份，拿走不谢。

multinetx.zip

这回秀一下python版的多层网络绘图，虽然比MuxViz差远了，但也可以一用。

===============更新@2020-10-31 20:51:12=============
为了更好地绘制多层网络，可以采用multinetx进行建模，使用Pymnet进行可视化展示。

先说一下基本思路：multinetx的底层是实现是networkX的Graph，可以实现一个方法，将MultinetX对象转化为文件，格式示例如下：

 *.edges file format:
node1 layer1 node2 layer2 weight (optional)
node1 layer1 node2 layer2 weight (optional)

e.g.,

1 1 1 1 1
1 1 2 1 1
1 1 10 1 1
1 1 12 1 1
...

net = MultilayerNetwork(aspects=1,fullyInterconnected=False,directed=False)
net[i,j,s,r]=w

draw_figure(
        net,
        ...
)

G1 = nx.Graph()
G2 = nx.Graph()
G3 = nx.Graph()

G2.add_edges_from([(1,2),(2,3),(4,6),(4,5)])
G1.add_edges_from([(1,3),(4,5)])
G3.add_edges_from([(3,4)])

# 自定义的multinetX构造函数
M = mx.build_multilayer_graph([G1,G2,G3])
# 使用multinetx 风格绘制的多层网络
mx.show_multilayer_network(M)

# 导出为临时文件
filepath = './tmp.edges'
mx.save_edges(M, filepath)

# pyment 读入临时文件并转化为 pymnet 对象
net = mx.read_edges(filepath)

# 使用 pymnet 风格绘制多层网络
net.draw_multilayer(M)

def build_multilayer_graph(list_of_layers, seeds):
    '''
    @description: 通过多个单层网络以及种子节点构建融合后的多层网络
    @param : seeds为层间连边，是一个由字典构成的列表, e.g. {(0, 0):1.0}
    @return: 
    '''
    M = MultilayerGraph(list_of_layers=list_of_layers)
    # layer1nodes = list(list_of_layers[0])
    # layer2nodes = list(list_of_layers[1])

    num_nodes = M.get_num_nodes()
    # nodes = M.get_list_of_nodes()
    # multi_nodes = M.get_list_of_multinetx_nodes()

    # 构建超邻接矩阵
    adj_block = lil_matrix(np.zeros((num_nodes, num_nodes)))
    # multi_seeds = [(n,m):seeds[n] for n in seeds for m in seeds if m != n]
    for s in seeds:
        # e = [multi_nodes[key] for key in s]
        for r in s:
            for c in s:
                if r != c:
                    adj_block[r, c] = seeds[s]

    adj_block += adj_block.T
    M.set_inter_edges_weight(adj_block)
    return M

1. http://nikos.techprolet.com/multinetx-v1-0/