Preserving the degree-sequence while generating SBM

Hello Tiago,

I am trying to generate a stochastic block model but with the
degree-sequence preserved. I am fine even if the degree-distribution is
preserved instead of the exact sequence. I tried the following:

def prob(a, b):
    if a == b :
        return 0.999
        return 0.001

g, bm = gt.random_graph(N, lambda: 1 + np.random.poisson(5), model =
"blockmodel-degree", directed = False,
block_membership=np.random.randint(0, b, N), edge_probs = prob)

However, this generates an ER graph. What can I do to retain the

Thank you

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I forgot to mention that 'b' in np.random.randint(0, b, N) is the number of

Thank you

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I believe this is a bug with the alias method used. Try with the option
"alias=False", and don't forget to use a large value of "n_iter".

I'll provide a fix in git soon.


That worked! Thanks a lot.


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