51 lines
1.5 KiB
Python
51 lines
1.5 KiB
Python
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"""
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===============
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Degree Analysis
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===============
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This example shows several ways to visualize the distribution of the degree of
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nodes with two common techniques: a *degree-rank plot* and a
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*degree histogram*.
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In this example, a random Graph is generated with 100 nodes. The degree of
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each node is determined, and a figure is generated showing three things:
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1. The subgraph of connected components
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2. The degree-rank plot for the Graph, and
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3. The degree histogram
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"""
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import networkx as nx
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import numpy as np
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import matplotlib.pyplot as plt
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G = nx.gnp_random_graph(100, 0.02, seed=10374196)
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degree_sequence = sorted((d for n, d in G.degree()), reverse=True)
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dmax = max(degree_sequence)
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fig = plt.figure("Degree of a random graph", figsize=(8, 8))
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# Create a gridspec for adding subplots of different sizes
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axgrid = fig.add_gridspec(5, 4)
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ax0 = fig.add_subplot(axgrid[0:3, :])
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Gcc = G.subgraph(sorted(nx.connected_components(G), key=len, reverse=True)[0])
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pos = nx.spring_layout(Gcc, seed=10396953)
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nx.draw_networkx_nodes(Gcc, pos, ax=ax0, node_size=20)
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nx.draw_networkx_edges(Gcc, pos, ax=ax0, alpha=0.4)
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ax0.set_title("Connected components of G")
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ax0.set_axis_off()
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ax1 = fig.add_subplot(axgrid[3:, :2])
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ax1.plot(degree_sequence, "b-", marker="o")
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ax1.set_title("Degree Rank Plot")
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ax1.set_ylabel("Degree")
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ax1.set_xlabel("Rank")
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ax2 = fig.add_subplot(axgrid[3:, 2:])
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ax2.bar(*np.unique(degree_sequence, return_counts=True))
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ax2.set_title("Degree histogram")
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ax2.set_xlabel("Degree")
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ax2.set_ylabel("# of Nodes")
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fig.tight_layout()
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plt.show()
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