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
returnS
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