def question3():
"""
calculates and plots running time of fast_targeted_order
versus targeted_order
for a series of increasingly larger UPA graphs
"""
t_o_times = []
f_t_o_times = []
graph_sizes = []
for num_nodes in range(10, 1000, 10):
graph_sizes.append(num_nodes)
curr_graph = app2_create_graphs.upa(num_nodes, 5)
t_o_start = time.time()
app2_provided.targeted_order(curr_graph)
t_o_times.append(time.time() - t_o_start)
f_t_o_start = time.time()
fast_order.fast_targeted_order(curr_graph)
f_t_o_times.append(time.time() - f_t_o_start)
pyplot.plot(graph_sizes, t_o_times, color='red', linestyle='-',
marker=None, label='targeted_order')
pyplot.plot(graph_sizes, f_t_o_times, color='blue', linestyle='-',
marker=None, label='fast_targeted_order')
pyplot.title('Comparison of algorithm running times: Desktop Python')
pyplot.xlabel('number of nodes in upa graph')
pyplot.ylabel('running time in seconds')
pyplot.grid(True)
pyplot.legend(loc='upper left')
pyplot.show()
def question1():
"""
create 3 graphs - subject each to the same random attack order
plot the resulting largest cc for each
"""
network_graph = app2_provided.load_graph(app2_provided.NETWORK_URL)
attack_order = random_order(network_graph)
network_resil = bfs_visited.compute_resilience(network_graph, attack_order)
er_graph = app2_create_graphs.undirected_er(1239, 0.002)
attack_order = random_order(er_graph)
while count_edges(er_graph) < 2900 or count_edges(er_graph) > 3190:
er_graph = app2_create_graphs.undirected_er(1239, 0.002)
er_resil = bfs_visited.compute_resilience(er_graph, attack_order)
upa_graph = app2_create_graphs.upa(1239, 3)
attack_order = random_order(upa_graph)
upa_resil = bfs_visited.compute_resilience(upa_graph, attack_order)
pyplot.plot(range(1240), network_resil, color='red', linestyle='-',
marker=None, label='Network Graph')
pyplot.plot(range(1240), er_resil, color='blue', linestyle='-',
marker=None, label='ER Graph, p=0.002')
pyplot.plot(range(1240), upa_resil, color='green', linestyle='-',
marker=None, label='UPA Graph, m=3')
pyplot.title('Question 1\nComparison of Random Attack Graph Degradation')
pyplot.xlabel('Number of Nodes Removed')
pyplot.ylabel('Largest Connected Component')
pyplot.legend(loc='upper right')
pyplot.grid(True)
pyplot.show()
return network_resil, er_resil, upa_resil
