[graph-tool] Functions producing small-world / scale free / ER networks
Mehdi Khoury
mehdi.khoury at gmail.com
Sat Aug 6 04:17:47 CEST 2011
Hi Tiago,
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.
See code:
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
def get_n_levels_neighbours(maxn,n,k):
l=range(n-(k/2),n)
l+=range(n+1,n+(k/2)+1)
for i in range(len(l)):
if (l[i]<0):
l[i]=((l[i])%(maxn-1))+1
if(l[i]>(maxn-1)):
l[i]=l[i]%maxn
return l
I have noticed that the boost library has a small-world graph generator
see:
http://www.boost.org/doc/libs/1_40_0/libs/graph/doc/small_world_generator.html
Would there be any way to call that inside graph-tool as a faster
alternative?
Cheers!
Mehdi
On Thu, Aug 4, 2011 at 10:07 AM, Tiago de Paula Peixoto <tiago at skewed.de>wrote:
> On 08/02/2011 03:03 PM, mehdi wrote:
> > small-world -> n : The number of nodes , k : Each node is connected to
> > k nearest neighbors in ring topology , p: The probability of rewiring
> > each edge
>
> There no function for this yet. It is in my TODO list. But you can
> create this easily by creating a 1D lattice (a ring) with the lattice()
> function, and than randomly rewire a portion of the edges.
>
> > scale-free -> n : Number of nodes, m : Number of edges to attach from
> a new
> > node to existing nodes
>
> This is implemented in the price_network() function.
>
> > ER -> n : The number of nodes , p : Probability for edge creation.
>
> This is also in my TODO list, although it is very easy to
> implement. Note that, for most purposes, this is equivalent to
> generating a random graph with random_graph() and using a poisson degree
> distribution with the appropriate average.
>
> Cheers,
> Tiago
>
> --
> Tiago de Paula Peixoto <tiago at skewed.de>
>
>
> _______________________________________________
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> graph-tool at skewed.de
> http://lists.skewed.de/mailman/listinfo/graph-tool
>
>
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