Old engine for Continuous Time Bayesian Networks. Superseded by reCTBN. 🐍
https://github.com/madlabunimib/PyCTBN
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22 lines
505 B
22 lines
505 B
"""
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===========
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Eigenvalues
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===========
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Create an G{n,m} random graph and compute the eigenvalues.
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"""
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import matplotlib.pyplot as plt
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import networkx as nx
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import numpy.linalg
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n = 1000 # 1000 nodes
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m = 5000 # 5000 edges
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G = nx.gnm_random_graph(n, m)
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L = nx.normalized_laplacian_matrix(G)
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e = numpy.linalg.eigvals(L.A)
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print("Largest eigenvalue:", max(e))
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print("Smallest eigenvalue:", min(e))
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plt.hist(e, bins=100) # histogram with 100 bins
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plt.xlim(0, 2) # eigenvalues between 0 and 2
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plt.show()
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