#####################################
# Code for answering question 4 of the application
# Create a graph using the DPA-algorithm
generated_graph = make_graph_DPA(27700, 13)
# Compute and print the edges generated
tot_edges = 0
for key, value in generated_graph.items():
tot_edges += len(value)
print
print "Total number of edges created =", tot_edges
print
# Compute the in-degree distribution
in_degree_dist = degree.in_degree_distribution(generated_graph)
# Normalize the in-degree distribution and create lists of the data to plot
total = 0
citation_number = []
citation_distr = []
for dummy_key, value in in_degree_dist.items():
total += value
for key, value in in_degree_dist.items():
citation_number.append(key)
citation_distr.append(float(value) / total)
# Create plot of the result
print
print "====> Creating the plot."
plt.loglog(citation_number, citation_distr, 'b.', linestyle='None')
counter = 1
for key, value in citation_graph.items():
print counter, key, value
counter += 1
if counter > 10:
break
print
#####################################
# Code for answering question 1 of the application
# Compute the in-degree distribution
in_degree_dist = degree.in_degree_distribution(citation_graph)
# Normalize the in-degree distribution and create lists of the data to plot
total = 0
citation_number = []
citation_distr = []
for dummy_key, value in in_degree_dist.items():
total += value
for key, value in in_degree_dist.items():
citation_number.append(key)
citation_distr.append(float(value) / total)
# Create plot of the result
plt.loglog(citation_number, citation_distr, 'b.', linestyle='None')
plt.grid(True)
plt.title("Plot 1: loglog plot citation distribution")
