<｜begin▁of▁sentence｜>### Instruction:
As an event planner coordinating a sophisticated conference for experts in network science, I'm engaging with a scenario where two distinct, yet interconnected sessions are being designed to simulate network behavior. The goal is to model two separate networking communities within each session, with each community having an equal number of attendees. In our model, the likelihood of attendees networking within their own community is set to be higher than with the other community, reflecting common interest groups.

To add a layer of complexity, we're looking to mirror networking patterns across these two sessions; essentially, if an interaction (or 'edge') occurs within one session, there's a high likelihood it will be mirrored in the other. 

To achieve this, I'd like to understand how we could apply the 'graspologic.simulations.sbm_corr' function to generate these correlated networking graphs based on our predefined probabilities for intra- and inter-community interactions, and the desired correlation between the two sessions' networking graphs. The success of the conference depends on crafting these highly correlated yet distinct communities to foster meaningful exchanges, and this function seems pivotal in orchestrating that outcome.
### 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 probability

G1, G2 = sbm_corr(n, p, r, directed=False, loops=False)
print('Graph G1:\n', G1)
print('Graph G2:\n', G2)
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