Indeed out_degree() is very very slow. I just replaced it with a constant
and it finished the computation in 0.5 seconds compared to the 2.7
minutes!!! But I guess what you mentioned is also slow since 'graph.
degree_property_map('out')[i]' since assumes 'i' is a vertex object and not
the index of the vertex. In that case I need to call graph.vertex(index)
and get the vertex and then call degree_property_map('out')[vertex]. I
guess this is still very slow!
On Friday, March 21, 2014 10:10:37 AM UTC+1, Guillaume Gay wrote:
>
> 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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