Hi Tiago,

Just checking to see the status on this. I've tried the newer version (2.44) but am still having the same issues. Your proposed solution of copying the state and adding the overlap work for the non-nested block models, but I was having trouble with the nested version. I am also now running into an issue where I can only run non degree corrected sbms, otherwise it hangs (logs attached). If you have any suggestions, I would really appreciate it. 

Thanks,

Eli

-> = copied state adding the following options, then reran mcmc

+ = defined in minimization function followed by mcmc

NDC = yes

NDC+Overlap = slow

NDC->Overlap = yes

NDC+Nested = slow

NDC+Nested+Overlap = slow

NDC+Nested->Overlap (not implemented)

NDC+Nested+Overlap -> DC = slow

DC+Overlap+Nested = hangs (This is the goal)

DC+Overlap = hangs

DC+Nested->Overlap (not implemented)

On Thu, Nov 11, 2021 at 3:16 AM Tiago de Paula Peixoto <tiago@skewed.de> wrote:

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 <tiago@skewed.de>
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PhD Candidate, Phil Bourne's lab

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