<｜begin▁of▁sentence｜>### Instruction:
A colleague in the field of network science is keen to construct two linked networks that mirror a shared grouping pattern. Each network is to be segmented into two clusters comprising three nodes apiece. The likelihood of a connection within the same group is set at 50%, while nodes from disparate groups have a mere 10% chance of linkage. Additionally, there is a desire to maintain a 30% similarity rate for any particular connection appearing in both networks. These networks are to be bidirectional and exempt from self-connecting loops. How might one employ the 'sbm_corr' function present in the 'graspologic.simulations' module for the generation of such intertwined networks?
### Response:
import numpy as np
from graspologic.simulations import sbm_corr

np.random.seed(3)
n = [3, 3]  # Number of vertices in each of the two communities
p = [[0.5, 0.1], [0.1, 0.5]]  # Edge probabilities between communities
r = 0.3  # Correlation coefficient

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

print('Graph G1 adjacency matrix:\n', G1)
print('Graph G2 adjacency matrix:\n', G2)
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