Am 11.11.21 um 03:03 schrieb Eli Draizen:
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.
Indeed, I will add more examples about this. Could you please open an
issue in the website so I don't forget?
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)
This is correct. But note that the "sampling=True" option is no longer
needed.
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.
The new version will contain a much faster code for the overlapping case!
But in the mean-time, what you can do is to fit the non-overlapping
model first, and use that as a starting point to the MCMC with overlap.
You do that simply by doing:
state = state.copy(state_args=dict(overlap=True))
Best,
Tiago
--
Tiago de Paula Peixoto <ti...@skewed.de>
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