<｜begin▁of▁sentence｜>### Instruction:
In the innovative sphere of materials engineering, especially when exploring the realms of computational modeling for materials analysis, constructing and analyzing structural analogies can often illuminate pathways to understanding material properties and behaviors. Imagine we are embarking on a project that aims to explore the hierarchical structuring of composite materials, drawing parallels to binomial trees to understand the distribution and arrangement of composite elements within a material.

Your task, as part of this exploratory project, is to employ NetworkX, a tool pivotal in computational modeling, to construct a binomial tree representation. This model is not just an abstract mathematical structure but serves as a direct analogy to the hierarchical organization within certain composite materials. For a given structural level designated as n=3, this construct will encompass a total of 2^3 elements, symbolically representing individual composite particles or material components, interconnected through 2^3 - 1 linkages, mirroring the interactions or bonds between these components.

The practical challenge here is twofold. Firstly, to translate the conceptual binomial tree structure into a computational model using NetworkX, effectively mapping out the elements (nodes) and their interconnections (edges) in a manner that mirrors our understanding of material composites. Secondly, to visually represent this model for n=3, illustrating the organizational hierarchy and interrelationships between components, thereby providing insights into potential properties and behaviors of composite materials at this level of complexity.

This exploration not only advances our theoretical understanding and practical capabilities in materials engineering but also paves the way for innovative computational models that can predict and enhance the performance and functionality of advanced materials.
### Response:
import networkx as nx

# Create a binomial tree of order 3
n = 3
B = nx.binomial_tree(n)

# Draw the binomial tree (This requires matplotlib)
import matplotlib.pyplot as plt
nx.draw(B, with_labels=True)
plt.show()
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