def question4_plot():
"""
Loads the three graphs in the problem, calculates a sequence of targeted
attacks using fast_targeted_attack and calculates the resilience of each
graph based on this attack. Plots the results.
"""
# Loading the provided computer network example
example_graph = graph_provided.load_graph(NETWORK_URL)
# Creating a random ER graph with 1239 nodes and p = .004
er_graph = make_random_ugraph(1239, .004)
# Creating an UPA graph, m=12
upa_graph = mod_upa.create_UPA_graph(1239, 2)
# Calculating attack sequences with the provided targeted_order
#example_attack = graph_provided.targeted_order(example_graph)
#er_attack = graph_provided.targeted_order(er_graph)
#upa_attack = graph_provided.targeted_order(upa_graph)
# Calculating attack sequences with fast_targeted_order
example_attack = fast_targeted_order(example_graph)
er_attack = fast_targeted_order(er_graph)
upa_attack = fast_targeted_order(upa_graph)
# Calculating resilience
example_resilience = mod_resilience.compute_resilience(example_graph,
example_attack)
er_resilience = mod_resilience.compute_resilience(er_graph,
er_attack)
upa_resilience = mod_resilience.compute_resilience(upa_graph,
upa_attack)
# Plotting the outcome
xvals = range(1240)
plt.plot(xvals, example_resilience, '-c',
label='computer network example (provided)')
plt.plot(xvals, er_resilience, '-y',
label='generated ER graph (p = .004)')
plt.plot(xvals, upa_resilience, '-m',
label='generated UPA graph (m = 2)')
plt.legend(loc='upper right')
plt.title('Graphs resilience to targeted attack')
plt.xlabel('Nodes removed')
plt.ylabel('Biggest connected element size')
plt.show()
def question3_plot():
"""
Calculates a series of targeted attacks using both functions (provided and
the fast targeted implemented one), computes the running time of these
processes and plots the outcome
"""
# Initializing the data series
xvals = range(10 , 1000, 10)
targeted_times = []
fast_targeted_times = []
for size in range(10, 1000, 10):
upa_graph = mod_upa.create_UPA_graph(size, 5)
# Calculating the running time of targeted_attack for this iteration
start_clock = time.clock()
graph_provided.targeted_order(upa_graph)
end_clock = time.clock() - start_clock
targeted_times.append(end_clock * 1000)
# Calculating the running time of fast_targeted_attack for this iteration
start_clock = time.clock()
fast_targeted_order(upa_graph)
end_clock = time.clock() - start_clock
fast_targeted_times.append(end_clock * 1000)
# Plotting the outcome
plt.plot(xvals, targeted_times, '-c',
label='targeted_order times (O(n**2))')
plt.plot(xvals, fast_targeted_times, '-y',
label='fast_targeted_order times (O(n))')
plt.legend(loc='upper left')
plt.title('Processing times to calculate attacks (desktop Python)')
plt.ylabel('Running time (milliseconds)')
plt.xlabel('Graph size')
plt.show()
