def eval_system(g, system=None, paras=None):
seeds = []
for i in g.nodes_iter():
if g.node[i]['seed'] == 1:
seeds.append(i)
if system == 'integro':
t = time.clock()
ranks = integro.run_integro(g, seeds=seeds)
print('eval time total integro {}'.format(time.clock() - t))
elif system == 'votetrust':
t = time.clock()
votetrust.vote_assignment(g, seeds)
ranks = votetrust.vote_combined(g, paras.d)
print('eval time total votetrust {}'.format(time.clock() - t))
elif system == 'sybilframe':
t = time.clock()
sybilframe.inferPosteriorsEdgeImproveNew(g)
ranks = sybilframe.getRanks(g)
print('eval time total sybilframe {}'.format(time.clock() - t))
real = [g.node[i]['label'] for i in range(len(g.nodes()))]
b = benchmarks.Benchmarks(real, ranks)
return b
def potential_capacity_per_distance():
seeds = (0, 500, 1000, 1500)
results = []
for d in (0.8, 0.9, 0.95, 0.99):
per_distance = defaultdict(list)
for i in range(10):
g = graph_creation.create_directed_smallWorld(2000, 40)
votetrust.vote_assignment(g, seeds)
votetrust.vote_propagation_mat(g, d)
for n in g.nodes_iter():
shortest = 1000
for s in seeds:
length = len(nx.shortest_path(g, s, n)) - 1
if length < shortest:
shortest = length
per_distance[shortest].append(g.node[n]['vote_capacity'] /
(len(g.out_edges(n)) + 1))
for key, value in per_distance.items():
per_distance[key] = np.mean(per_distance[key])
result_d = list(per_distance.values())
print(result_d)
results.append(result_d)
with open('../results/votetrust/seed_attack_turnout.csv', 'w+') as f:
w = csv.writer(f, lineterminator='\n')
w.writerows(results)
def calc_trust_distribution():
g = graph_creation.create_directed_smallWorld(2000, 40)
votetrust.vote_assignment(g, [0, 500, 1000])
d = 1 - 3 / len(g.nodes())
votetrust.vote_propagation_mat(g, d)
votetrust.vote_aggregation(g)
votes = [x[1]['vote_capacity'] for x in list(g.node.items())]
with open('../results/votetrust/trustDist.csv', 'w+') as f:
w = csv.writer(f, lineterminator='\n')
w.writerow(votes)
def eval_most_common_friends():
results = []
g_0 = graph_creation.create_directed_smallWorld(2000, 40)
ATTACKER = len(g_0.nodes())
for i in range(20):
n = i * 5 + 1
results.append([n])
print('run')
for j in range(10):
g = g_0.copy()
attacks_votetrust.attack_most_common_friends(g, n)
votetrust.vote_assignment(g, [0, 500, 1000])
votetrust.vote_propagation_mat(g, d=0.99)
votetrust.vote_aggregation(g)
results[i].append(g.node[ATTACKER]['p'])
with open('../results/votetrust/mostCommon.csv', 'w+') as f:
w = csv.writer(f, lineterminator='\n')
w.writerows(results)
def most_common(mode='strict'):
results = []
g_0 = graph_creation.create_directed_smallWorld(2000, 40)
nx.set_node_attributes(g_0, 'label', 0)
for i in range(5):
n = i * 5 + 1
results.append([n])
for j in range(1):
g = g_0.copy()
attacks_votetrust.attack_most_common_friends(g,
n,
mode=mode,
boosting=True,
dummies=[50])
ATTACKER = len(g.nodes()) - 1
votetrust.vote_assignment(g, [0, 90, 160])
votetrust.vote_propagation_mat(g, d=0.99)
votetrust.vote_aggregation(g)
results[i].append(g.node[ATTACKER]['p'])
print(results)
"""