<｜begin▁of▁sentence｜>### Instruction:
Certainly, let me put this into a bit of context for you from a tax consultant's perspective. Imagine you're charged with the task of scrutinizing a complex network of financial transactions that are intricately interconnected, resembling a vast web of economic activity. It's just like poring over the detailed financial statements of a corporate client who's looking for ways to minimize tax liabilities legally. Now, you need to examine a subset of these transactionsÃ¢ÂÂlet's say a sample of tenÃ¢ÂÂfor a more focused analysis to provide a concrete recommendation.

In the world of graphs and network data, this translates to taking that big, unwieldy graph of connections and distilling it down to its essence. It's a bit like those sampling techniques we use when we're trying to use a manageable set of data to make accurate predictions or draw insights.

So here's our situation rephrased in our professional lingo: Provided with a complex ledger of transactions that's digitally represented in a file named 'littleballoffur6.sparse6', could you employ a techniqueÃ¢ÂÂakin to the RandomWalkSampler in graph theoryÃ¢ÂÂto extract a representative 'mini-ledger' of 10 transactions? This exercise would be particularly useful for a detailed audit. Just to tie it back to our original problem, if this network were documented in a .gml file format, your task would be to name the file accordingly.
### Response:
import networkx as nx
from littleballoffur import RandomWalkSampler
G = nx.read_sparse6('littleballoffur6.sparse6')
sampler = RandomWalkSampler(number_of_nodes=10)
sampled_graph = sampler.sample(G)
print(sampled_graph.edges(), sampled_graph.nodes())
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