if __name__ == "__main__":
import time
start = time.time()
G = nx.read_gpickle("../../graphs/hep.gpickle")
print 'Read graph G'
print time.time() - start
model = "MultiValency"
if model == "MultiValency":
ep_model = "range"
elif model == "Random":
ep_model = "random"
elif model == "Categories":
ep_model = "degree"
# get propagation probabilities
Ep = dict()
with open("Ep_hep_%s1.txt" %ep_model) as f:
for line in f:
data = line.split()
Ep[(int(data[0]), int(data[1]))] = float(data[2])
I = 1000
S = newGreedyIC(G, 10, Ep)
print S
print avgIAC(G, S, Ep, I)
def mapAvgSize (S):
return runIAC.avgIAC(G, S, Ep, I)
def mapAvgSize(S):
return runIAC.avgIAC(G, S, Ep, I)
def mapAvgSize (args):
G, S, Ep, I = args
return avgIAC(G, S, Ep, I)
