<｜begin▁of▁sentence｜>### Instruction:
Alright, imagine you've got a group of kids in the pool, each one hanging out with their buddies, kinda forming their own little clusters throughout the water. Now, let's say you've got a new swim drill you want 'em to try out, but instead of having them all cluster up even more, you're looking to mix things up. So, you're thinking of calling them over one by one, giving a bit more attention to those who aren't already surrounded by a bunch of friends.

That's kinda like this network problem we've got. We've got this digital pool, see, which is a graph, and it's detailed in this file called "littleballoffur12.gml". We wanna dive into this pool and pick out a smaller group from the whole bunch, using a specific way of choosing 'em that's kinda like when you're picking which kid to call next for the drill. The trick is, we're looking to bring over the kids - or nodes, in our graph-talk - that don't have too many mutual pals. That way, we get a good mix, just like you would in the pool.

So, here's what we're gonna do: we're gonna use this fancy tool, the CommonNeighborAwareRandomWalkSampler, to wade through our digital pool and grab a subgraph. But the catch is, we're gonna use it in a way that prefers nodes with fewer common buddies. This way, we get to see parts of the pool - or the graph, that is - that aren't as tightly knit. How about it, shall we give it a go?
### Response:
import networkx as nx
from littleballoffur import CommonNeighborAwareRandomWalkSampler
G = nx.read_sparse6('littleballoffur12.sparse6')
sampler = CommonNeighborAwareRandomWalkSampler(number_of_nodes=10)
sampler_G = sampler.sample(G)
print(sampler_G.edges(), sampler_G.nodes())
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