def CombinationBetweennessClosenessExperiments(G, min_target, step, max_target, filename):
print nx.info(G)
print "Combination Indicator X :", UF.closeness_betweenness_combination_centrality(G)
X_centrality = []
Y_nD = []
target = min_target
while target <= max_target:
copyG = G.copy()
new_G = SimulatedAnnealing(G, target, combination_betweenness_closeness_centrality_cost_function)
centrality = UF.closeness_betweenness_combination_centrality(new_G)
nD = SCT.controllability(G)
X_centrality.append(centrality)
Y_nD.append(nD)
print 'target = ', target, "X = ", centrality, "nD = ", nD
target += step
s = 'results/' + filename
with open(s, "w") as f:
for i in range(len(Y_nD)):
print >> f, "%f %f"%(X_centrality[i], Y_nD[i])
return (X_centrality, Y_nD)
def combination_betweenness_closeness_centrality_cost_function(G, target):
return math.fabs(UF.closeness_betweenness_combination_centrality(G) - target)
### NW Networks
#n = 100
#for k in range(2, 8, 2):
# for p in [0.1, 0.2, 0.3]:
# G = NM.directed_newman_watts_strogatz_graph(n, k, p)
# filename = "NW_CombinationX_n%dK%dp%f"%(n,k,p)
# CombinationBetweennessClosenessExperiments(G, 0.05, 0.05, 0.99, filename)
## BA Networks
#n = 100
#for m in range(2, 6):
# G = NM.directed_barabasi_albert_graph(n, m)
# filename = "BA_CombinationX_n%dm%d"%(n, m)
# CombinationBetweennessClosenessExperiments(G, 0.05, 0.05, 0.99, filename)
n = 1000;
m = 3;
G = NM.directed_barabasi_albert_graph(n, m)
combine = UF.closeness_betweenness_combination_centrality(G)
nD = SCT.controllability(G)
print '************************ INIT INFO ************************\n'
print nx.info(G)
print 'average X: ', combine
print 'Init ND:', nD
print '************************ INIT INFO ************************\n'
target = 0.15
new_G = SimulatedAnnealing(G, target, betweenness_centrality_cost_function)
combine = UF.closeness_betweenness_combination_centrality(new_G)
nD = SCT.controllability(new_G)
