def attack_ordered(ugraphs):
'''
attack the ugraph with the ordered nodes
'''
result = []
for ugraph in ugraphs:
nodes_ordered = targeted_order(ugraph)
resilience = compute_resilience(ugraph, nodes_ordered)
result.append(resilience)
data_file = open('data_resilience_ordered.p', 'wb')
pickle.dump(result, data_file)
data_file.close()
return 'data_resilience_ordered.p'
def attack_ordered(ugraphs):
'''
attack the ugraph with the ordered nodes
'''
result = []
for ugraph in ugraphs:
nodes_ordered = targeted_order(ugraph)
resilience = compute_resilience(ugraph, nodes_ordered)
result.append(resilience)
data_file = open('data_resilience_ordered.p', 'wb')
pickle.dump(result, data_file)
data_file.close()
return 'data_resilience_ordered.p'
def attack(ugraphs):
'''
simulate an attack to the given ugraph
shuffle the nodes, and remove then one by one
return the cc_size of the ugraph after removed the nodes.
save the lists into a file.
'''
result = []
for ugraph in ugraphs:
nodes_random = random_order(ugraph)
resilience = compute_resilience(ugraph, nodes_random)
result.append(resilience)
data_file = open('data_resilience.p', 'wb')
pickle.dump(result, data_file)
data_file.close()
return 'data_resilience.p'
def attack(ugraphs):
'''
simulate an attack to the given ugraph
shuffle the nodes, and remove then one by one
return the cc_size of the ugraph after removed the nodes.
save the lists into a file.
'''
result = []
for ugraph in ugraphs:
nodes_random = random_order(ugraph)
resilience = compute_resilience(ugraph, nodes_random)
result.append(resilience)
data_file = open('data_resilience.p', 'wb')
pickle.dump(result, data_file)
data_file.close()
return 'data_resilience.p'
