# Load the network-graph print "==========> Loading the network-graph" network_graph = nagen.load_graph(nagen.NETWORK_URL) # Generate the ER-graph print print "==========> Generating the ER-graph" er_graph = nagen.make_graph_ER(1239, 0.0040) # Generate the UPA-graph print print "==========> Generating the UPA-graph" upa_graph = nagen.make_graph_UPA(1239, 3) # Generate the attack orders for the three networks using fast_targeted_order print print "==========> Generating attack schedules" network_attack = nagen.targeted_order(network_graph) er_attack = nagen.targeted_order(er_graph) upa_attack = nagen.targeted_order(upa_graph) # Attack the computer-network print print "==========> Attacking the computer-network" network_resilience = ca.compute_resilience(network_graph, network_attack) # Attack the ER-network print print "==========> Attacking the ER-network" er_resilience = ca.compute_resilience(er_graph, er_attack) # Attack the UPA-network print print "==========> Attacking the UPA-network" upa_resilience = ca.compute_resilience(upa_graph, upa_attack)
num_nodes = [0]
time_targeted_order = [0.0]
time_fast_targeted_order = [0.0]
# Generate all the point for the plot
for nodes in range(10, 1000, 10):
# Fill in the nodes
num_nodes.append(nodes)
# Generate the graph to be used
print "=====> Generating graph with", nodes, "nodes."
UPA_graph = nagen.make_graph_UPA(nodes, 5)
# Measure the time for targeted_order
print
print "=====> Run targeted_order for", nodes, "nodes."
start_time = time.time()
attack_list = nagen.targeted_order(UPA_graph)
end_time = time.time()
time_targeted_order.append(end_time - start_time)
# Measure the time for fast_targeted_order
print
print "=====> Run fast_targeted_order for", nodes, "nodes."
start_time = time.time()
attack_list = nagen.fast_targeted_order(UPA_graph)
end_time = time.time()
time_fast_targeted_order.append(end_time - start_time)
print
# Create plot of the result
plt.plot(num_nodes, time_targeted_order, '-b', label='Time targeted_order')
plt.plot(num_nodes, time_fast_targeted_order, '-r', label='Time fast_targeted_order')
plt.legend(loc='upper left')
