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"\n# Eigenvalues\n\nCreate an G{n,m} random graph and compute the eigenvalues.\n"
]
},
{
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"execution_count": null,
"metadata": {
"collapsed": false
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"source": [
"import matplotlib.pyplot as plt\nimport networkx as nx\nimport numpy.linalg\n\nn = 1000 # 1000 nodes\nm = 5000 # 5000 edges\nG = nx.gnm_random_graph(n, m, seed=5040) # Seed for reproducibility\n\nL = nx.normalized_laplacian_matrix(G)\ne = numpy.linalg.eigvals(L.toarray())\nprint(\"Largest eigenvalue:\", max(e))\nprint(\"Smallest eigenvalue:\", min(e))\nplt.hist(e, bins=100) # histogram with 100 bins\nplt.xlim(0, 2) # eigenvalues between 0 and 2\nplt.show()"
]
}
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