import lpIM_nobigM import mipIM_nobigM import calculate_LT # paramters of IM S = 6 T = 10 fname = '/bkfrat-GraphML/BKFRAB.GraphML' # 'SAMPIN.GraphML' # get directed weighted network g = read_graphml.preprocess(fname) print 'g is a DAG:', g.is_dag() # influence maximization - original size, source, target, weight = len(g.vs), [e.source for e in g.es], [e.target for e in g.es], [w for w in g.es['normalized inweight']] ssCplex = cplex_tiny.optimize(S, T, size, source, target, weight) # influence maximization - original ssMip = mipIM_nobigM.optimize(S, T, size, source, target, weight) # calculate expected spread calculate_LT.run(ssCplex, S, T, size, source, target, weight) print 'seed set selected by Cplex' calculate_LT.run(ssMip, S, T, size, source, target, weight) print 'seed set selected by MIP_nobigM'
S = 2
T = 10
fname = '/bkfrat-GraphML/BKFRAB.GraphML' # 'SAMPIN.GraphML'
# get directed weighted network
g = read_graphml.preprocess(fname)
print 'g is a DAG:', g.is_dag()
# influence maximization - original
size, source, target, weight = len(g.vs), [e.source for e in g.es], [
e.target for e in g.es
], [w for w in g.es['normalized inweight']]
ssCplex = cplex_tiny.optimize(S, T, size, source, target, weight)
inweights = []
outweights = []
for v in g.vs:
inweights.append([(e.source, e['normalized inweight'])
for e in g.es(_target=v.index)])
outweights.append([(e.target, e['normalized inweight'])
for e in g.es(_source=v.index)])
# influence maximization - benders decomposition
sepflag = 0
sBenders = bendersIM_CB.optimize(S, T, sepflag, inweights, outweights)
# calculate expected spread
calculate_LT.run(ssCplex, S, T, size, source, target, weight)
print 'seed set selected by Cplex'
calculate_LT.run(sCombinatorial, S, T, size, source, target, weight)
print 'seed set selected by imcomplete Benders'
# double check
# sort nodes by sum(out-weight)
print '*********************************************************************************************'
print 'Double check through intuition (sorted out-weights)'
def reverse_comp(x, y):
if x[1] > y[1]:
return -1
if x[1] < y[1]:
return 1
return 0
node_sumw = []
for v in g.vs:
node_sumw.append(
(v.index, sum(g.es(_source=v.index)['normalized inweight'])))
node_sumw.sort(reverse_comp)
for i in xrange(5):
print node_sumw[i]
# calculate expected spread
calculate_LT.run(ssCplex, S, T, size, source, target, weight)
print 'seed set selected by Cplex'
calculate_LT.run(sBenders, S, T, size, source, target, weight)
print 'seed set selected by imcomplete Benders'
calculate_LT.run(ssBenders, S, T, size, source, target, weight)
print 'seed set selected by EXACT Benders'
def calculate_spread(ss, size, source, target, weight):
# calculate expected spread
return calculate_LT.run(ss, S, T, size, source, target, weight)
S = 2
T = 10
fname = '/bkfrat-GraphML/BKFRAB.GraphML' # 'SAMPIN.GraphML'
# get directed weighted network
g = read_graphml.preprocess(fname)
print 'g is a DAG:', g.is_dag()
# influence maximization - original
size, source, target, weight = len(g.vs), [e.source for e in g.es], [e.target for e in g.es], [w for w in g.es['normalized inweight']]
ssCplex = cplex_tiny.optimize(S, T, size, source, target, weight)
inweights = []
outweights = []
for v in g.vs:
inweights.append([(e.source, e['normalized inweight']) for e in g.es(_target = v.index)])
outweights.append([(e.target, e['normalized inweight']) for e in g.es(_source = v.index)])
# influence maximization - benders decomposition
sepflag = 0
sBenders = bendersIM_CB.optimize(S, T, sepflag, inweights, outweights)
# calculate expected spread
calculate_LT.run(ssCplex, S, T, size, source, target, weight)
print 'seed set selected by Cplex'
calculate_LT.run(sCombinatorial, S, T, size, source, target, weight)
print 'seed set selected by imcomplete Benders'
def calculate_spread(ss, size, source, target, weight):
# calculate expected spread
return calculate_LT.run(ss, S, T, size, source, target, weight)
