<｜begin▁of▁sentence｜>### Instruction:
In the glamorous world of fashion modeling, where the spotlight often shines on the distinct styles of two influential designers, imagine we're observing an exclusive social gathering of 300 loyal followers for each fashion icon. Although both groups mingle at the event, the interaction is noticeably more vibrant within each group than between the two. 

To capture the essence of this social interplay, akin to a photographer capturing the perfect ensemble, consider the use of a technique akin to choosing the right filter for a photoshoot. Picture us employing the equivalent of a sophisticated camera lens, Classical Multidimensional Scaling (cMDS), to distill the essence of these intricate social ties into a simple, two-dimensional snapshot. Think of this as an attempt to transition from a complex, multi-layered fabric weave to a smooth, two-toned silk print in order to highlight the distinction between the admirers of the two designers.

With this dynamic imagery, how might we translate the bustling energy of the soirée and the nuanced interactions between these fashionable cliques into a visual tableau that reflects the connections as elegantly as a runway showcases the season's trends? Let's envision using the adjacency matrix—a matrix capturing the presence or absence of conversation, like the threads of communication weaving through the party—as our map to lay the foundation for this creative visual display using cMDS. How do we proceed to paint this complex network of relations onto a flat canvas that still retains the depth of the social fabric at this high-fashion event?
### Response:
```python
import numpy as np
from graspologic.embed import ClassicalMDS
from graspologic.simulations import sbm

# Simulate a graph using Stochastic Block Model
c1_size, c2_size = 300, 300
n = c1_size + c2_size
p = [[0.1, 0.01], [0.01, 0.1]]
labels = [0] * c1_size + [1] * c2_size
G = sbm([c1_size, c2_size], p)

# Assume dissimilarity matrix is the adjacency matrix
# for the sake of this example (usually it would be a different measure)
dissimilarity_matrix = nx.to_numpy_array(G)

# Apply Classical Multidimensional Scaling (cMDS)
cmds = ClassicalMDS(dissimilarity='precomputed', n_components=2)
X_cMDS = cmds.fit_transform(dissimilarity_matrix)

# The list of 2D coordinates representing each node's position in the reduced space
print(X_cMDS)
```
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