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

Connected components of G, Degree Rank Plot, 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()

Total running time of the script: ( 0 minutes 0.273 seconds)

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