<｜begin▁of▁sentence｜>### Instruction:
Hey there, readers! As a reporter always on the hunt for cutting-edge stories, I've stumbled upon something that's truly piqued my interest within the realms of network analysis. Picture this: scientists and data enthusiasts are constantly exploring the complex interconnections of social networks, biological systems, and even intricate infrastructure. It's a wild web of nodes and links!

Now imagine we've got this digital ecosystem, a network graph, if you will. To make sense of it all, researchers utilize various tools, one of which is called the DiffusionTreeSampler. It's like a high-tech seine net, skimming just the right parts of this data ocean to catch a meaningful pattern Ã¢ÂÂ in this case, a tree structure composed of a subset of the network's nodes.

So here's the scoop Ã¢ÂÂ we've got this specific graph, think of it as the blueprint of our digital ecosystem, saved in the cutting-edge sparse6 format, affectionately dubbed "littleballoffur8.sparse6". What these data wizards want to do is whip up a sample, a mini-model if you will, that's a tree with exactly 10 nodes. It's like pruning a bonsai with mathematical precision. 

Now, translate this to our tech-savvy crowd: How can these researchers use the DiffusionTreeSampler to extract a subgraph, a tiny but representative tree of knowledge, with precisely 10 nodes from the original expansive network provided by littleballoffur8.sparse6? And let's add a flair of real-world application Ã¢ÂÂ if the original question involved a gml file, let's say for argument's sake, the file would be known as littleballoffur8.gml.
### Response:
import networkx as nx
from littleballoffur import DiffusionTreeSampler
G = nx.read_sparse6('littleballoffur8.sparse6')
sampler = DiffusionTreeSampler(number_of_nodes=10)
sampler_G = sampler.sample(G)
print(sampler_G.edges(), sampler_G.nodes())
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