O.rka - 1 year ago 218

Python Question

I'm trying to get into creating network graphs and generating sparse matrices from them. From the wikipedia

`Laplacian matrix`

`networkx`

`adjacency matrix`

`network graph`

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.

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

Recommended for you: Get network issues from **WhatsUp Gold**. **Not end users.**

Answer Source

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)
```

Here's the documentation.

And from adjacency matrix to graph:

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

Here's the documentation.