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
    else:
        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 block-structure?

Thank you
--
Snehal M. Shekatkar
Pune
India