<｜begin▁of▁sentence｜>### Instruction:
In the political landscape, we recurrently notice that tightly-knit factions or interest groups emerge, each composed of a handful of key influencers. Envision, for a moment, a hypothetical situation in which we are probing into three separate political factions, each with precisely four members who are closely interconnected within their respective group. As a political analyst seeking to dissect the intricate web of relations within this triad of factions, how might we engage NetworkX as a tool to meticulously construct and graphically represent this social framework? 

Should our analysis necessitate a granular inspection of this nexus, we may encapsulate the affiliations and allegiances within a GML file. Suppose the filename is "political_factions.gml", this would form the basis for our visual and quantitative analysis. How would we proceed with the modeling in NetworkX, ensuring that it accurately echoes the dense clustering found within each political clique, whilst keeping them discernable as separate entities within the broader network diagram?
### Response:
import networkx as nx

# Create a caveman graph with 3 cliques, each of size 4
G = nx.caveman_graph(3, 4)

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