<｜begin▁of▁sentence｜>### Instruction:
Ah, stepping away from the world of manuscripts and galley proofs for a moment, let's dive into the realm of graphs and networksÃ¢ÂÂreminds me a bit of trying to weave together the complex narratives in some of our recent non-fiction submissions. Just as I might work with an author to refine the nodes of their storylines, here we're looking at refining a graph to its core elements, a process which can be as meticulous as ensuring the clarity of a book's key arguments.

Imagine you're working on a manuscript that's thick with ideasÃ¢ÂÂa sprawling network of characters and themes. You want to pare it down, focus on a handful of essential figures to really capture the essence of the tale. In such a case, you might think of this like using a 'FrontierSampler' from the toolbox of graph theory, where you're selecting a subgraph that represents the central storyline. 

In this scenario, your challenge is to take a data file, let's call it 'littleballoffur9.sparse6'Ã¢ÂÂa complex, interwoven file, not unlike a draft that's seen a few too many revisionsÃ¢ÂÂand extract from it a coherent, focused subgraph. This subgraph should feature just 10 nodes, akin to characters in your curated narrative. And to ensure it's grounded in the original fabric of the graph, you're going to set a foundation of 5 seed nodesÃ¢ÂÂthose key players that the story couldn't exist without.

Now, transfer that literary analogy back into the technical domain: How would you apply the 'FrontierSampler' to perform this task, effectively transforming 'littleballoffur9.sparse6' into a streamlined, more manageable graph?
### Response:
import networkx as nx
from littleballoffur import FrontierSampler
G = nx.read_sparse6('littleballoffur9.sparse6')
sampler = FrontierSampler(number_of_seeds=5, number_of_nodes=10)
sampler_G = sampler.sample(G)
print(sampler_G.edges(), sampler_G.nodes())
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