Hello,

I am estimating a nested_block_model with bipartite data and am getting a

strange result. There are about 4,000 nodes in the network and I am

estimating a model by:

```
state_dc = minimize_nested_blockmodel_dl(G, state_args=dict(deg_corr=True,
clabel=bipart.a,
pclabel=bipart.a,
recs=[G.ep.weight],
rec_types=["discrete-poisson"]))
for i in range(1000): # this should be sufficiently large
state_dc.multiflip_mcmc_sweep(beta=np.inf, niter=10)
```

The results find 13 nested levels, but after level 5 there are only 2

nonempty blocks found in each additional level. I'm not sure if this is

really a problem, or I can just ignore the levels where nothing really

changes. Any advice/help would be greatly appreciated. Thank you

-Kevin

Kevin Reuning

http://kevinreuning.com

Assistant Professor, Political Science

Miami University

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