def EigenvectorCentralityExperiment(G, min_target, max_target, filename):
print nx.info(G)
print 'average eigenvector centrality: ', UF.average_eigenvector_centrality(G)
X_eigenvector_centrality = []
Y_nD = []
target = min_target
while target <= max_target:
copyG = G.copy()
new_G = SimulatedAnnealing(copyG, target, eigenvector_centrality_cost_function)
eigenvector_centrality = UF.average_eigenvector_centrality(new_G)
nD = SCT.controllability(new_G)
X_eigenvector_centrality.append(eigenvector_centrality)
Y_nD.append(nD)
print "target = ", target, " EC = ", eigenvector_centrality, 'nD = ', nD
target += 0.01
s = 'results/' + filename
with open(s, "w") as f:
for i in range(len(Y_nD)):
print >> f, "%f %f"%(X_eigenvector_centrality[i], Y_nD[i])
return (X_eigenvector_centrality, Y_nD)
def eigenvector_centrality_cost_function(G, target):
return math.fabs(UF.average_eigenvector_centrality(G) - target)
