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#!/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>
# All rights reserved.
# BSD license.
from networkx import *
try:
import numpy.linalg
eigenvalues=numpy.linalg.eigvals
except ImportError:
raise ImportError("numpy can not 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
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