<｜begin▁of▁sentence｜>### Instruction:
In a study on social networks, researchers are interested in exploring the effect of an intervention on the structure of the network. They have divided the network into three subgroups and expect the intervention to alter the probability of connections among members within the same subgroup and across different subgroups. They wish to create two correlated networks that represent the pre-intervention (control) and post-intervention (treated) states. How can they simulate such correlated networks using a stochastic block model that preserves a certain level of correlation between the networks' structures?
### Response:
import numpy as np
from graspologic.simulations import sbm_corr

# Define the community sizes and connection probabilities
n = [50, 50, 50]  # 3 communities of 50 nodes each
p = [[0.5, 0.1, 0.1],
     [0.1, 0.5, 0.1],
     [0.1, 0.1, 0.5]]  # Higher probability for intra-community edges
r = 0.3  # Correlation between graph pairs

# Generate a pair of correlated stochastic block model graphs
G1, G2 = sbm_corr(n, p, r, directed=False, loops=False)

# G1 and G2 are adjacency matrices representing the two graphs
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