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"""
===========
Eigenvalues
===========
Create an G{n,m} random graph and compute the eigenvalues.
"""
import matplotlib.pyplot as plt
import networkx as nx
import numpy.linalg
n = 1000 # 1000 nodes
m = 5000 # 5000 edges
G = nx.gnm_random_graph(n, m)
L = nx.normalized_laplacian_matrix(G)
e = numpy.linalg.eigvals(L.A)
print("Largest eigenvalue:", max(e))
print("Smallest eigenvalue:", min(e))
plt.hist(e, bins=100) # histogram with 100 bins
plt.xlim(0, 2) # eigenvalues between 0 and 2
plt.show()
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