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
--
PhD Candidate, Phil Bourne's lab
University of Virginia
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