<｜begin▁of▁sentence｜>### Instruction:
Given the task to study the correlation between two social groups within a community, a network analyst wants to generate two correlated graphs where each graph represents a social group. There are 3 individuals in each group, and the analyst hypothesizes that there are strong interactions within the groups and weaker interactions between the groups. Additionally, the analyst believes that the same individual from each group tends to behave similarly across the two social environments. Can you write a Python code using graspologic to generate a pair of correlated graphs based on pre-defined group sizes and connection probabilities, and then convert the resulting adjacency matrices into networkx graphs to examine the edges?
### Response:
import numpy as np
import networkx as nx
from graspologic.simulations import sbm_corr

np.random.seed(3)
n = [3, 3]
p = [[0.5, 0.1], [0.1, 0.5]]
r = 0.3
(G1, G2) = sbm_corr(n, p, r, directed=False, loops=False)

# Create a networkx graph from the adjacency matrix
G1_nx = nx.from_numpy_array(G1)
G2_nx = nx.from_numpy_array(G2)

# Show the edges of the first graph
print('Edges of G1:', G1_nx.edges())
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