<｜begin▁of▁sentence｜>### Instruction:
Certainly, imagine you're sitting in the comfort of your office surrounded by charts and the hum of the stock market. As a financial advisor, you're always looking for innovative ways to analyze networks of financial transactions to help guide your clients towards making informed investment decisions. Picture a complex web of companies, interconnected through transactions, mergers, and partnerships, resembling a graph.

You're intrigued by the concept that not all connections are equal Ã¢ÂÂ much like not all stocks have the same value or influence on a portfolio. You decide to employ a similar strategy to what you use for diversifying assets; focusing on the most influential nodes within a financial network graph. You recall that PageRank is a tool used to measure the importance of webpages, and now you're considering its merit in evaluating the weight of nodes within your financial graph.

So, let's translate this to your current task: You have a financial network graph stored in a `.sparse6` file, let's name it `financial_network.sparse6`, and you want to extract a subgraph that represents the most significant connections, based on PageRank Ã¢ÂÂ the equivalent of handpicking the blue-chip stocks for a portfolio. Specifically, you aim to sample a subgraph comprising 10 nodes which stand out by their PageRank score. How would you go about doing this using littleballoffur's PageRankBasedSampler?
### Response:
import networkx as nx
from littleballoffur import PageRankBasedSampler
G = nx.read_sparse6('littleballoffur11.sparse6')
sampler = PageRankBasedSampler(number_of_nodes=10)
sampler_G = sampler.sample(G)
print(sampler_G.edges(), sampler_G.nodes())
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