<｜begin▁of▁sentence｜>### Instruction:
In the course of designing a communication network for our new spacecraft model, we're utilizing a stochastic block model to simulate potential network layouts. This model has generated a graph with two distinct modules, akin to departments in our spacecraft design, with 50 nodes each. For the nodes representing the same department, there’s a 50% chance they’ll share a communication link, while nodes from different departments have only a 10% chance of link formation.

We need to visualize and analyze this model more intuitively. Can we discuss how we could apply an adjacency spectral embedding technique to project the complex network onto a 2-dimensional space? This would be akin to simplifying a wiring diagram for easier analysis, without losing the essential connections that define our spacecraft's communication architecture.
### Response:
import networkx as nx
from graspologic.embed import AdjacencySpectralEmbed
from graspologic.simulations import sbm

# Simulate a stochastic block model graph
n_communities = [50, 50]  # 50 nodes in each of two communities
p_matrix = [[0.5, 0.1], [0.1, 0.5]]  # Probabilities of community interconnections
G = sbm(n_communities, p_matrix)

# Initialize the AdjacencySpectralEmbed object
ase = AdjacencySpectralEmbed(n_components=2, n_elbows=2, check_lcc=True)

# Fit and transform the graph
embedding = ase.fit_transform(G)

# The embedding is a 2D representation of the graph's nodes
print(embedding)
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