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
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