NetworkX function to get centralization (for a network) from centrality (degree, closeness, betweenness, eigenvector)

getCentralizationAllTypes.py

def getCentralization(centrality, c_type):
	
	c_denominator = float(1)
	
	n_val = float(len(centrality))
	
	print (str(len(centrality)) + "," +  c_type + "\n")
	
	if (c_type=="degree"):
		c_denominator = (n_val-1)*(n_val-2)
		
	if (c_type=="close"):
		c_top = (n_val-1)*(n_val-2)
		c_bottom = (2*n_val)-3	
		c_denominator = float(c_top/c_bottom)
		
	if (c_type=="between"):
		c_denominator = (n_val*n_val*(n_val-2))
		
	if (c_type=="eigen"):

		'''
		M = nx.to_scipy_sparse_matrix(G, nodelist=G.nodes(),weight='weight',dtype=float)
		eigenvalue, eigenvector = linalg.eigs(M.T, k=1, which='LR') 
		largest = eigenvector.flatten().real
		norm = sp.sign(largest.sum())*sp.linalg.norm(largest)
		centrality = dict(zip(G,map(float,largest)))
		'''
		
		c_denominator = sqrt(2)/2 * (n_val - 2)
		
		
		
	
	#start calculations	
		
	c_node_max = max(centrality.values())


	c_sorted = sorted(centrality.values(),reverse=True)
	
			
	
	print ("max node" + str(c_node_max) + "\n")

	c_numerator = 0

	for value in c_sorted:
		
		if c_type == "degree":
			#remove normalisation for each value
			c_numerator += (c_node_max*(n_val-1) - value*(n_val-1))
		else:
			c_numerator += (c_node_max - value)
	
	print ('numerator:' + str(c_numerator)  + "\n")	
	print ('denominator:' + str(c_denominator)  + "\n")	

	network_centrality = float(c_numerator/c_denominator)
	
	if c_type == "between":
		network_centrality = network_centrality * 2
		
	return network_centrality