def BetweennessCentralityExperiment(G, min_target, max_target, filename):
print nx.info(G)
print 'average betweenness centrality: ', UF.average_betweenness_centrality(G)
X_betweenness_centrality = []
Y_nD = []
target = min_target
while target <= max_target:
copyG = G.copy()
new_G = SimulatedAnnealing(copyG, target, betweenness_centrality_cost_function)
betweenness_centrality = UF.average_betweenness_centrality(new_G)
nD = SCT.controllability(new_G)
X_betweenness_centrality.append(betweenness_centrality)
Y_nD.append(nD)
print "target = ", target, " BC = ", betweenness_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_betweenness_centrality[i], Y_nD[i])
return (X_betweenness_centrality, Y_nD)
N_rm = int(N * remove_fraction)
nodes = nx.nodes(G)
print "Before Removing:\n"
print "N = ", N
print "L = ", L
random.shuffle(nodes)
rm_nodes = nodes[0:N_rm]
G.remove_nodes_from(rm_nodes)
print "\n after removing:"
print "N = ", G.number_of_nodes()
print "L = ", G.number_of_edges()
print "<k> = ", float(2 * G.number_of_edges()) / G.number_of_nodes()
avg_deg = UF.average_degree(G);
print "<k>: ", avg_deg;
avg_bet = UF.average_betweenness_centrality(G);
print "<B>: ", avg_bet;
#APL = nx.average_shortest_path_length(G)
#print "APL: ", APL
N_core = nx.core_number(G)
core_values = N_core.values()
leaves = [x for x in core_values if x <= 1]
print "#leaves:", len(leaves)
print "#core:", G.number_of_nodes() - len(leaves)
print "#core/#N:", (G.number_of_nodes() - len(leaves))/float(G.number_of_nodes())
def betweenness_centrality_cost_function(G, target):
return math.fabs(UF.average_betweenness_centrality(G) - target)
