I've tried to use the following code to plot the degree distribution of the networkx.DiGraph
G
:
def plot_degree_In(G):
in_degrees = G.in_degree()
in_degrees=dict(in_degrees)
in_values = sorted(set(in_degrees.values()))
in_hist = [list(in_degrees.values()).count(x) for x in in_values]
plt.figure()
plt.grid(False)
plt.loglog(in_values, in_hist, 'r.')
#plt.loglog(out_values, out_hist, 'b.')
#plt.legend(['In-degree', 'Out-degree'])
plt.xlabel('k')
plt.ylabel('p(k)')
plt.title('Degree Distribution')
plt.xlim([0, 2*100**1])
But then I realized that this is not the proper way to do it and so I changed it to:
def plot_degree_dist(G):
degree_hist = nx.degree_histogram(G)
degree_hist = np.array(degree_hist, dtype=float)
degree_prob = degree_hist/G.number_of_nodes()
plt.loglog(np.arange(degree_prob.shape[0]),degree_prob,'b.')
plt.xlabel('k')
plt.ylabel('p(k)')
plt.title('Degree Distribution')
plt.show()
But this gives me an empty plot with with no data in it.
One way of printing the (in- plus out-)degree histogram with test code:
import matplotlib.pyplot as plt
import networkx as nx
def plot_degree_dist(G):
degrees = [G.degree(n) for n in G.nodes()]
plt.hist(degrees)
plt.show()
plot_degree_dist(nx.gnp_random_graph(100, 0.5, directed=True))
The number of bins for the histogram can be adjusted by adding a second parameter to plt.hist
.