<｜begin▁of▁sentence｜>### Instruction:
Imagine we're looking to investigate the decision-making pathways within a small, tightly-knit market community, which for the sake of our model, we'll represent using a 3-regular Platonic Cubical Graph. This compact network consists of just 8 individual actors (nodes) and 12 transactions (edges) that connect them. Our objective is to leverage NetworkX to not only construct but also visually represent this graph, as this will allow us to examine the structure and interplay of these entities. Could you walk me through the process we'd use to achieve this with NetworkX, ensuring our analysis is both comprehensive and visually insightful?
### Response:
import networkx as nx

# Generate a cubical graph
G = nx.cubical_graph()

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