Hi,

There is a propertymap that contains the out degrees of each vertex, it might be much faster to access it, i.e.:

`i_degree = graph.degree_property_map('out')[i]`

I guess I would also iterate over the edges rather than the indices of the adjacency matrix...

G.

Le 21/03/2014 09:44, Hang Mang a écrit :

Maybe graph.vertex(i).out_degree() is slow itself? If so, should I store all the degrees in a matrix then?

On Friday, March 21, 2014 9:09:23 AM UTC+1, Hang Mang wrote:

I have a graph with 1034 vertices and 53498 edges. I'm manually computing the preferential attachement index for the vertices, and other indices. I'm aware that graph-tool has that implemented but I'm doing it for personal stuff. However I noticed that my computations are very slow. It took 2.7 minutes to compute that for the mentioned graph. I'm not sure if it's my algorithm that is slow or the something is wrong with graph-tool. I would be very thankful if someone could have a little look into my code.

def pa(graph):

"""

Calculates Preferential Attachment index.

Returns S the similarity matrix.

"""

A = gts.adjacency(graph)

S = np.zeros(A.shape)

for i in xrange(S.shape[0]):

for j in xrange(S.shape[0]):

i_degree = graph.vertex(i).out_degree()

j_degree = graph.vertex(j).out_degree()

factor = i_degree * j_degree

S[i,j] = factor

return S
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