<｜begin▁of▁sentence｜>### Instruction:
In preparation for the upcoming issue of our esteemed journal, we are seeking to present a feature on network dynamics that encompasses a discussion of the Barabási–Albert model. Could you please formulate a manuscript that includes a practical analysis demonstrating the construction and graphical representation of this model within the context of a small-scale network?

Specifically, we are looking at a network consisting of 20 nodes, epitomizing the growth process of the network through preferential attachment, whereby each new node is introduced in a manner that it connects preferentially to 2 existing nodes of higher degree. This scenario vividly represents the 'rich-get-richer' phenomenon in network theory and would offer our readership a concrete understanding of the mechanics behind scale-free networks.

Please ensure that the visualization is effectively communicated and possibly accompanied by illustrative figures to enhance the comprehensibility of the concept for our readership. This analysis will substantially contribute to our section dedicated to empirical studies, providing a valuable resource for scholars and researchers interested in network theory's intricacies.
### Response:
import networkx as nx
import matplotlib.pyplot as plt

# Parameters
n = 20  # Number of nodes
m = 2   # Number of edges to attach from a new node to existing nodes
seed = 42  # Seed for random number generator

# Use the Barabási–Albert model to generate a graph
ba_graph = nx.barabasi_albert_graph(n, m, seed=seed)

# Draw the graph
pos = nx.spring_layout(ba_graph, seed=seed)
nx.draw(ba_graph, pos, with_labels=True)
plt.show()
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