Dear community, I have been following this thread as have a similar predicament - trying to run the nested block model on a graph of 1mil nodes and 12 mil edges. Since I have read of the computational cost from others, I have got access to a server with 500Gb RAM with ~120 CPU threads. Based upon this graph complexity and your prior experience, which approach you would aim for: 1) minimize_nested_blockmodel_dl(g) or 2) prime an empty object and MCMC equilibrate with multi-flip: bs = [np.zeros(1)]*6 state=NestedBlockState(g=g,bs=bs,sampling=True) gt.mcmc_equilibrate(state,multiflip=True) Am very grateful for the guidance…! (I wonder how long this will take to run…??) James
On 24 Feb 2020, at 18:40, Tiago de Paula Peixoto <tiago@skewed.de> wrote:
Am 24.02.20 um 19:32 schrieb Davide Cittaro:
(Remember to replace mcmc_sweep() with multiflip_mcmc_sweep() in the code above. I had forgotten to make this change the last release...)
Does this mean that one should also set "multiflip=True" in mcmc_equilbrate?
Yes.
-- Tiago de Paula Peixoto <tiago@skewed.de> _______________________________________________ graph-tool mailing list graph-tool@skewed.de https://eur01.safelinks.protection.outlook.com/?url=https%3A%2F%2Flists.skewed.de%2Fmailman%2Flistinfo%2Fgraph-tool&data=02%7C01%7C%7C3b3a0d8d77904e2eb56e08d7b9591fc8%7C569df091b01340e386eebd9cb9e25814%7C0%7C0%7C637181664763877491&sdata=izkn35h55P2quQwzqI4AaSS7Zk%2BO%2FY6Hhc9Fdmo38D8%3D&reserved=0