<｜begin▁of▁sentence｜>### Instruction:
As a flight instructor, you're all about the precision of maneuvers and understanding the dynamics of a controlled airspace, where multiple pilots interact according to strict protocols. Now, envision transferring this concept to network science, where you aim to model a theoretical airspace with two squadrons of aircraft, each squadron consisting of 50 pilots (or in this case, members). Your task is to sketch a flight plan that encapsulates the communication patterns within each squadron and between the two squadrons, akin to radio comms where squadron members are more likely to communicate internally but do still have cross-squadron chatter.

Your flight plan must consider different probabilities for in-squadron vs. cross-squadron interactions and maintain a similarity in the communication pattern for both groups, somewhat reflective of having two groups performing coordinated maneuvers with interlinked communication strategies.

To chart out this scenario, you'll be utilizing the `sbm_corr` function from the graspologic toolkit, which will act as your radar, allowing you to simulate the correlated networks of social interactions among these pilots. This will also include the 'visual flight rules' for this simulation represented through network visualization, enabling you to see how densely each squadron is talking among themselves as well as with the other squadron. Keep in mind, this simulation would effectively resemble the planning of a joint exercise where two squadrons need to operate both in harmony and with their unique internal commands.
### Response:
import numpy as np
from graspologic.simulations import sbm_corr

# Define the community sizes and inter-connectivity matrix
n = [50, 50]
p = [[0.5, 0.1], [0.1, 0.5]]

# Define the correlation coefficient between the graphs
r = 0.3

# Generate the correlated graphs
G1, G2 = sbm_corr(n, p, r, directed=False, loops=False)

# Use NetworkX to convert the adjacency matrices into graph objects
import networkx as nx
G1_graph = nx.from_numpy_array(G1)
G2_graph = nx.from_numpy_array(G2)

# View the first graph with nodes labeled
nx.draw(G1_graph, with_labels=True)
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