1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
|
#!/usr/bin/env python
"""
Create an G{n,m} random graph and compute the eigenvalues.
Requires numpy or LinearAlgebra package from Numeric Python.
Uses optional pylab plotting to produce histogram of eigenvalues.
"""
__author__ = """Aric Hagberg (hagberg@lanl.gov)"""
__credits__ = """"""
# Copyright (C) 2004-2006 by
# Aric Hagberg <hagberg@lanl.gov>
# Dan Schult <dschult@colgate.edu>
# Pieter Swart <swart@lanl.gov>
# Distributed under the terms of the GNU Lesser General Public License
# http://www.gnu.org/copyleft/lesser.html
from networkx import *
try:
import numpy.linalg
eigenvalues=numpy.linalg.eigvals
except ImportError:
try:
import LinearAlgebra
eigenvalues=LinearAlgebra.eigenvalues
except ImportError:
raise ImportError,"Neither numpy nor Numeric can be imported."
try:
from pylab import *
except:
pass
n=1000 # 1000 nodes
m=5000 # 5000 edges
G=gnm_random_graph(n,m)
L=generalized_laplacian(G)
e=eigenvalues(L)
print "Largest eigenvalue:",max(e)
print "Smallest eigenvalue:",min(e)
# plot with matplotlib if we have it
# shows "semicircle" distribution of eigenvalues
try:
hist(e,bins=100) # histogram with 100 bins
xlim(0,2) # eigenvalues between 0 and 2
show()
except:
pass
|
