<｜begin▁of▁sentence｜>### Instruction:
Imagine you're in the hushed, calm environment of a library, surrounded by towering shelves lined with books that hold the collective knowledge of centuries. YouÃ¢ÂÂre the librarian, well-versed in managing vast collections of literature, skilfully organizing them to ensure every seeker finds precisely what they need. Each day, you sort through donations, meticulously catalog new arrivals, and update the digital database to keep track of every single tome under your care. In your world, where order and accessibility are paramount, you appreciate the value of a well-structured system.

Now, let's take that organizational savvy to the realm of online informationÃ¢ÂÂa different kind of library with digital graphs instead of books. We've got a digital graph contained within a 'littleballoffur4.sparse6' file, much like a unique volume in our vast collection. Let's say we're looking to exhibit a portion of this graph, highlighting its most influential components.

What we aim to do is akin to selecting 10 key texts from a special collection based on their popularity and reference rates. In our digital scenario, we would use a tool, the PageRankBasedSampler, to extract a subgraph of 10 nodes. These nodes are akin to our texts, and their popularity is determined by their PageRank scores, reflecting how frequently they are 'referenced' or interconnected within the larger graph.

So, how would we apply the PageRankBasedSampler to our graph from the 'littleballoffur4.sparse6' file to create this concentrated snapshot of the most significant nodes? If our question were about a GML file instead, the challenge would remain the same, only we'd refer to it by its GML file name.
### Response:
import networkx as nx
from littleballoffur import PageRankBasedSampler
G = nx.read_sparse6('littleballoffur4.sparse6')
sampler = PageRankBasedSampler(number_of_nodes=10)
sampler_G = sampler.sample(G)
print(sampler_G.edges(), sampler_G.nodes())
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