Laplacian.py

import math

import networkx as nx
import matplotlib.pyplot as plt

G = nx.Graph()
G.add_edges_from([('A', 'B'), ('B', 'C'), ('C', 'D'), ('D', 'E'), ('E', 'A'), ('A', 'D')])

L = nx.laplacian_matrix(G)
e = nx.laplacian_spectrum(G)
print "Largest eigenvalue:", max(e)
print "Smallest eigenvalue:", min(e)

plt.hist(e, bins=100)
plt.xlim(0, math.ceil(max(e)))
plt.show()

matrix.py

import numpy as np
import networkx as nx
import matplotlib.pyplot as plt


A = np.matrix([[0,1,0,0,0,0],[1,0,1,0,0,0],[0,1,0,1,1,1],[0,0,1,0,1,0],[0,0,1,1,0,1],[0,0,1,0,1,0]])
print A

G = nx.from_numpy_matrix(A)
nx.draw(G, with_labels=True)
plt.show()

e = np.linalg.eigvals(A)
print e

print "Largest eigenvalue:", max(e)
print "Smallest eigenvalue:", min(e)