使用networkx绘制Python中的二部图[duplicate]

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Bipartite graph in NetworkX 1 answer
二部图在NetworkX 1回答

I have an n1-by-n2 bi-adjacency matrix A of a bipartite graph. The matrix A is a scipy.sparse csc matrix. I would like to plot the bipartite graph using A in networkx. Assume that the nodes are colored according to their class labels called node_class. I could do the following:

我有一个1×n2的双邻接矩阵A。矩阵A是scipy。csc稀疏矩阵。我想在networkx中使用A来绘制两部分图。假设节点是根据它们的类标签node_class着色的。我可以这样做:

import networkx as nx
G = nx.from_numpy_matrix(A)
graph_pos = nx.fruchterman_reingold_layout(G)
degree = nx.degree(G)
nx.draw(G, node_color = node_class, with_labels = False, node_size = [v * 35 for v in degree.values()])

The above code works fine for a square dense adjacency matrix. However not for a non-square bi-adjacency matrix A. The error is:

上面的代码适用于一个方形密集的邻接矩阵。但对于非平方双邻接矩阵a则不是这样，误差为:

'Adjacency matrix is not square.'

Moreover the matrix A I have is a scipy.sparse matrix` because it is very large and have lots of zeros. So I would want to avoid making an (n1+n2)-by-(n1+n2) adjacency matrix by stacking A and adding zeros.

而且矩阵A是一个scipy。稀疏矩阵，因为它很大，有很多0。所以我想通过叠加A和加0来避免(n1+n2)-by-(n1+n2)邻接矩阵。

I checked the documentation of NetworkX for bipartite graphs, it does not mention how to plot bi-partite graph using bi-adjacency matrix, or create a graph using bi-adjacency sparse matrix. If someone could tell me how to plot the bipartite graph, that would be great!

我查看了两部分图的NetworkX文档，它没有提到如何使用bi-邻接矩阵来绘制bi-partite图，或者使用bi-邻接稀疏矩阵来创建图形。如果有人能告诉我如何绘制二部图，那就太好了!

2 个解决方案

#1

I don't believe there is a NetworkX function that creates a graph from a biadjacency matrix, so you'll have to write your own. (However, they do have a bipartite module you should check out.)

我不相信有一个NetworkX函数用一个双邻接矩阵来创建一个图，所以你必须自己写。(不过，他们确实有一个你应该检查的两部分模块。)

Here's one way to define a function that takes a sparse biadjacency matrix and converts it to a NetworkX graph (see the comments for explanation).

这里有一种方法来定义一个函数，该函数接受一个稀疏的双邻接矩阵并将其转换为一个NetworkX图(请参阅注释以获得解释)。

# Input: M scipy.sparse.csc_matrix
# Output: NetworkX Graph
def nx_graph_from_biadjacency_matrix(M):
    # Give names to the nodes in the two node sets
    U = [ "u{}".format(i) for i in range(M.shape[0]) ]
    V = [ "v{}".format(i) for i in range(M.shape[1]) ]

    # Create the graph and add each set of nodes
    G = nx.Graph()
    G.add_nodes_from(U, bipartite=0)
    G.add_nodes_from(V, bipartite=1)

    # Find the non-zero indices in the biadjacency matrix to connect 
    # those nodes
    G.add_edges_from([ (U[i], V[j]) for i, j in zip(*M.nonzero()) ])

    return G

See an example use case below, where I use nx.complete_bipartite_graph to generate a complete graph:

请参见下面的一个示例用例，其中我使用nx.complete_bipartite_graph生成一个完整的图:

import networkx as nx, numpy as np
from networkx.algorithms import bipartite
from scipy.sparse import csc_matrix
import matplotlib.pyplot as plt
RB = nx.complete_bipartite_graph(3, 2)
A  = csc_matrix(bipartite.biadjacency_matrix(RB, row_order=bipartite.sets(RB)[0]))
G = nx_graph_from_biadjacency_matrix(A)
nx.draw_circular(G, node_color = "red", with_labels = True)
plt.show()

And here's the output graph: 使用networkx绘制Python中的二部图[duplicate]

这是输出图:

#2

Here is a simple example:

这里有一个简单的例子:

import networkx as nx
import matplotlib.pyplot as plt
from networkx.algorithms import matching
%matplotlib inline

ls=[
[0,0,0,1,1],
[1,0,0,0,0],
[1,0,1,0,0],
[0,1,1,0,0],
[1,0,0,0,0]
]
g = nx.Graph()
a=['a'+str(i) for i in range(len(ls))]
b=['b'+str(j) for j in range(len(ls[0]))]
g.add_nodes_from(a,bipartite=0)
g.add_nodes_from(b,bipartite=1)

for i in range(len(ls)):
    for j in range(len(ls[i])):
        if ls[i][j] != 0:
            g.add_edge(a[i], b[j])
pos_a={}
x=0.100
const=0.100
y=1.0
for i in range(len(a)):
    pos_a[a[i]]=[x,y-i*const]

xb=0.500
pos_b={}
for i in range(len(b)):
    pos_b[b[i]]=[xb,y-i*const]

nx.draw_networkx_nodes(g,pos_a,nodelist=a,node_color='r',node_size=300,alpha=0.8)
nx.draw_networkx_nodes(g,pos_b,nodelist=b,node_color='b',node_size=300,alpha=0.8)

# edges
pos={}
pos.update(pos_a)
pos.update(pos_b)
#nx.draw_networkx_edges(g,pos,edgelist=nx.edges(g),width=1,alpha=0.8,edge_color='g')
nx.draw_networkx_labels(g,pos,font_size=10,font_family='sans-serif')
m=matching.maximal_matching(g)
nx.draw_networkx_edges(g,pos,edgelist=m,width=1,alpha=0.8,edge_color='k')

plt.show()

#1