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