<｜begin▁of▁sentence｜>### Instruction:
Good evening, viewers. In today's segment on modern network theory, we explore an intriguing concept in the realm of graph construction. Let's imagine a small town with eight intersections -- or for the mathematically inclined, eight nodes. The town council has a goal: to design a road system that's robust enough so that if any two roads are under construction, citizens can still travel between any two intersections.

This leads us to a fascinating challenge. Our town engineers have been tasked with creating a road map, also known as a Harary graph, that fulfills this requirement. They've informed us that the minimum number of roads needed to maintain such connectivity -- which they refer to as node connectivity -- is three.

So the question that we pose to our network-savvy viewers tonight is: Can our engineers craft a Harary graph, denoted as H_{3,8}, using the fewest possible roads while keeping our town as connected as the council desires? We're talking about a graph that's not just efficient, but also optimized for resilience.

Later in our program, we'll be taking an illustrative look at this very blueprint when our engineers furnish us with a visual representation of this sophisticated yet minimalist road network -- all brought to life using a cutting-edge tool called NetworkX. Stay tuned for this intellectual journey into the infrastructure of tomorrow.
### Response:
import networkx as nx

# Given the node connectivity k and the number of nodes n
# creates the Harary graph H_{k,n} that minimizes
# the number of edges with the given constraints.
k = 3
n = 8
H = nx.hkn_harary_graph(k, n)

# Draw the graph
import matplotlib.pyplot as plt
nx.draw(H, with_labels=True)
plt.show()
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