thanks for the feedback.
I have followed your advice for the scale-free and er network and there are no pb for these.
The watts-strogatz network version I have implemented works, but is very very slow.
def watts_strogatz_network(n,k,p):
g=lattice([n])
# make a ring
g.add_edge(g.vertex(0), g.vertex(n-1))
# add edges to k neighbours of each vertex in the ring
# k-1 neighbours if k is odd
if ((k%2!=0) and k>1):
k-=1
if k>2:
for v in range(n):
l_n=get_n_levels_neighbours(n,v,k)
for v_n in l_n:
if not g.edge(v_n,v):
g.add_edge(g.vertex(v), g.vertex(v_n))
# replace each edge u-v by an edge u-w with probability p
for u in range(n):
for v in g.vertex(u).all_neighbours():
if (random()<=p):
l1=range(n)
l2=get_n_levels_neighbours(n,u,k)
l=[i for i in l1 if i not in l2]
l.remove(u)
#print l
w=rd.choice(l)
while w==u or g.edge(u,w):
w=rd.choice(l)
g.remove_edge(g.edge(u, v))
g.add_edge(g.vertex(u), g.vertex(w))
return g