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> _______________________________________________ graph-tool mailing list -- graph-tool@skewed.de To unsubscribe send an email to graph-tool-leave@skewed.de