kind = sys.argv[1]
attack = {}
for d in range(1, 4):
for m in range(3, 4):
for p in range(1, 4):
v = "d" + str(d) + "m" + str(m) + "p" + str(p)
attack[v] = {}
attack[v]['dc'] = defaultdict(float)
attack[v]['ec'] = defaultdict(float)
attack[v]['bc'] = defaultdict(float)
for j in sample:
g = sim.graph_by_time(sim.retrieve_graph(kind, d, m, p, j), t)
strategies = {}
strategies['dc'] = sorted(nx.degree_centrality(g).items(), key=lambda x: x[1], reverse=True)[0:a]
strategies['ec'] = sorted(nx.eigenvector_centrality(g).items(), key=lambda x: x[1], reverse=True)[0:a]
strategies['bc'] = sorted(nx.betweenness_centrality(g).items(), key=lambda x: x[1], reverse=True)[0:a]
for strategy in strategies:
cg = copy.deepcopy(g)
for i in range(0,a):
if (attack[v][strategy][i]):
attack[v][strategy][i] += sim.average_inverse_geodesic(cg)
else:
attack[v][strategy][i] = sim.average_inverse_geodesic(cg)
id, degree = strategies[strategy][i]
import simlib as sim
import sys
import pickle
import random
degree = {}
n = 1001
sample = random.sample(range(1,500),100)
kind = sys.argv[1]
for d in range(1, 4):
for m in range(1, 6):
for p in range(1, 4):
v = "d" + str(d) + "m" + str(m) + "p" + str(p)
# degree array by time
degree[v] = {}
for j in range(1, sample):
g = sim.retrieve_graph(kind, d, m, p, j)
for t in range(1, n):
degree[v][t] = []
gt = sim.graph_by_time(g, t)
for node in gt.nodes():
degree[v][t].append(g.degree(node))
print "Done with: " + v
with open("./serial/time_degree_" + kind + "_" + v + ".obj", 'wb') as objfile:
pickle.dump(degree[v], objfile)
names[name] = [tmp[2]]
print ("loaded names...")
for name in names:
haig = {}
hcmp = {}
for t in range(0,attack):
haig[t] = 0.0
hcmp[t] = 0.0
random.shuffle(names[name])
for _ in range(0,sample):
j = names[name].pop()
G = nx.read_gpickle("graphs/"+name+"_"+j)
g = sim.graph_by_time(G, time)
nds = sorted(nx.degree_centrality(g).items(), key=lambda x: x[1], reverse=True)[0:attack]
# print nds
print ("\nWorking on "+j)
for i in range(0, attack):
geo = sim.average_inverse_geodesic(g)
haig[i] += geo
# hcmp[i] += nx.connected_component_subgraphs(g)[0].number_of_nodes()
nid, degree = nds[i]
g.remove_node(nid)
sys.stdout.write("-") #+str(geo))
sys.stdout.flush()
for t in range(0, attack):
haig[t] = float(haig[t])/sample
hcmp[t] = float(hcmp[t])/sample
