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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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