This package takes an undirected unweighted scipy.sparse adjacency matrix as an input and computes the GTOM(m) method, using m+1-step neighbors [1]. It's highly efficient and can be used for parallel computation by calling the function for only few nodes at a time.
[1] Gene network interconnectedness and the generalized topological overlap measure, A. M. Yip and S. Horvath, BMC Bioinformatics 2007 8:22
$ sudo python setup.py install
#!python
import matplotlib.pyplot as pl
import networkx as nx
from gtom import gtom
G = nx.Graph()
G.add_edges_from([(0,1),(1,2),(0,3),(0,4),(0,5),(0,7),
(1,3),(1,4),(1,6),(1,8),(1,9),(1,10),
(5,6),(7,8)])
print "nodes:", G.nodes()
pos = nx.spring_layout(G)
labels = { n:str(n+1) for n in G.nodes()}
nx.draw(G,pos=pos)
nx.draw_networkx_labels(G,pos=pos,labels=labels)
N = G.number_of_nodes()
A = nx.to_scipy_sparse_matrix(G)
print "recreate results from figure 3 in [1]"
print " |\t(i,j)=(1,2)\t(i,j)=(1,3)\t(i,j)=(2,3)"
print "---------------------------------------------------------"
for m in range(3):
T = gtom(A,m)
print " m = %d |\t%f\t%f\t%f" %(m,T[0,1],T[0,2],T[1,2])
print
print "computed only for nodes 1 and 2:"
print " |\t(i,j)=(1,2)\t(i,j)=(1,3)\t(i,j)=(2,3)"
print "---------------------------------------------------------"
for m in range(3):
T = gtom(A,m,indices=[0,1])
print " m = %d |\t%f\t%f\t%f" %(m,T[0,1],T[0,2],T[1,2])
pl.show()