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