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