从网络图有效地创建邻接矩阵(反之亦然)Python NetworkX

2022-01-19 00:00:00 python matrix networkx graph network-programming

问题描述

我正在尝试创建网络图并从中生成稀疏矩阵.从维基百科 拉普拉斯矩阵 示例中，我决定尝试使用 networkx

I'm trying to get into creating network graphs and generating sparse matrices from them. From the wikipedia Laplacian matrix example, I decided to try and recreate the following network graph using networkx

如何在邻接矩阵和网络图之间有效地转换?

How can one EFFICIENTLY convert between an adjacency matrix and a network graph?

例如，如果我有一个网络图，如何快速将其转换为邻接矩阵，如果我有一个邻接图，如何有效地将其转换为网络图.

For example, if I have a network graph, how can I quickly convert it to an adjacency matrix and if I have an adjacency graph how can I efficiently convert it to a network graph.

下面是我的代码，我觉得对于大型网络来说效率很低.

Below is my code for doing it and I feel like it's pretty inefficient for larger networks. 

#!/usr/bin/python

import networkx as nx
import numpy as np
import matplotlib.pyplot as plt
import scipy as sp
import pandas as pd

%matplotlib inline

#Adjacent matrix
adj_matrix = np.matrix([[0,1,0,0,1,0],[1,0,1,0,1,0],[0,1,0,1,0,0],[0,0,1,0,1,1],[1,1,0,1,0,0],[0,0,0,1,0,0]])
adj_sparse = sp.sparse.coo_matrix(adj_matrix, dtype=np.int8)
labels = range(1,7)
DF_adj = pd.DataFrame(adj_sparse.toarray(),index=labels,columns=labels)
print DF_adj

#   1  2  3  4  5  6
#1  0  1  0  0  1  0
#2  1  0  1  0  1  0
#3  0  1  0  1  0  0
#4  0  0  1  0  1  1
#5  1  1  0  1  0  0
#6  0  0  0  1  0  0

#Network graph
G = nx.Graph()
G.add_nodes_from(labels)

#Connect nodes
for i in range(DF_adj.shape[0]):
    col_label = DF_adj.columns[i]
    for j in range(DF_adj.shape[1]):
        row_label = DF_adj.index[j]
        node = DF_adj.iloc[i,j]
        if node == 1:
            G.add_edge(col_label,row_label)


#Draw graph
nx.draw(G,with_labels = True)

#DRAWN GRAPH MATCHES THE GRAPH FROM WIKI

#Recreate adjacency matrix
DF_re = pd.DataFrame(np.zeros([len(G.nodes()),len(G.nodes())]),index=G.nodes(),columns=G.nodes())
for col_label,row_label in G.edges():
    DF_re.loc[col_label,row_label] = 1
    DF_re.loc[row_label,col_label] = 1
print G.edges()
#[(1, 2), (1, 5), (2, 3), (2, 5), (3, 4), (4, 5), (4, 6)]

print DF_re
#   1  2  3  4  5  6
#1  0  1  0  0  1  0
#2  1  0  1  0  1  0
#3  0  1  0  1  0  0
#4  0  0  1  0  1  1
#5  1  1  0  1  0  0
#6  0  0  0  1  0  0

解决方案

如何从图转换为邻接矩阵:

How to convert from graph to adjacency matrix:

import scipy as sp
import networkx as nx
G=nx.fast_gnp_random_graph(100,0.04)
adj_matrix = nx.adjacency_matrix(G)

这里是文档.

从邻接矩阵到图:

H=nx.Graph(adj_matrix)  #if it's directed, use H=nx.DiGraph(adj_matrix)

这是文档.

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