hi,

here is a simple example where i run minimize_blockmodel_dl() 10 times in parallel using multiprocessing and collect the entropy. when i run this, i get the same value of entropy every single time.

```
import multiprocessing as mp
import numpy as np
import time
import graph_tool.all as gt
# load graph
g = gt.collection.data["celegansneural"]
N_iter = 10
def get_sbm_entropy():
np.random.seed()
state = gt.minimize_blockmodel_dl(g)
return state.entropy()
def _parallel_mc(iter=N_iter):
pool = mp.Pool(10)
future_res = [pool.apply_async(get_sbm_entropy) for _ in range(iter)]
res = [f.get() for f in future_res]
return res
def parallel_monte_carlo(iter=N_iter):
entropies = _parallel_mc(iter)
return entropies
parallel_monte_carlo()
```

result: [8331.810102822546, 8331.810102822546, 8331.810102822546, 8331.810102822546, 8331.810102822546, 8331.810102822546, 8331.810102822546, 8331.810102822546, 8331.810102822546, 8331.810102822546]

ultimately i would like to use this to keep entropy as well as the block membership vector for each iteration

any ideas?

cheers,

-sam