<｜begin▁of▁sentence｜>### Instruction:
A network researcher wants to generate two correlated graphs that share a similar community structure. Both graphs are to be divided into two communities with three vertices each. Within the same community, vertices have a 50% chance of being connected, whereas vertices belonging to different communities only have a 10% chance of having an edge between them. The researcher also wants to ensure that there's a 30% probability of any given edge existing in both graphs. The graphs should be undirected and should not contain self-loops. How can the 'sbm_corr' function from the 'graspologic.simulations' module be used to create such a pair of correlated graphs?
### 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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