Hi everyone,

I was wondering if it would be possible to provide some more examples of

how to run a nested mixed membership SBM with edge weights. The new version

seems to have removed the "overlap=True" option for state_args in the

minimize_* functions.

Is this the correct way to do it now?

import graph_tool as gta

import numpy as np

g = .... # build graph

e_score = .... #Set edge weights

state_args = dict(

deg_corr=deg_corr,

base_type=gta.inference.overlap_blockmodel.OverlapBlockState,

B=2*g.num_edges(), #B_max

deg_corr=True,

recs=[e_score],

rec_types=["real-normal"])

state = gta.inference.minimize_nested_blockmodel_dl(

g,

state_args=state_args,

multilevel_mcmc_args=dict(verbose=True))

# improve solution with merge-split

state = state.copy(bs=state.get_bs() + [np.zeros(1)] * 4, sampling=True)

for i in range(100):

if i%10==0: print(".", end="")

ret = state.multiflip_mcmc_sweep(niter=10, beta=np.inf, verbose=True)

I am currently running this for a fully connected bipartite graph with 3454

nodes and 55008 edges. I understand it would take longer than the

non-overlapping version, but do you have any suggestions on how to speed it

up? The non-overlapping version takes about 15 minutes, while the

overlapping version is still running after 1 day.

Thanks for your help,

Eli

attachment.html (2.02 KB)