<｜begin▁of▁sentence｜>### Instruction:
Well, hello there! You know, as a Museum Educator, I spend a lot of my days thinking about how we can illustrate complex ideas in ways that really resonate with our visitors. Picture this: we've got a new interactive exhibit on the fascinating world of networks and graph theory. It's a bit like how we try to understand the intricate web of histories behind each and every artifact under our roof - everything is connected!

But now let's say we want to showcase a particular feature of our exhibit - imagine we have a digital representation of an ancient relic's provenance as a vast network, akin to a set of interlocking stories or a map of connections stretching back in time. This network is stored in what we call a .sparse6 fileÃ¢ÂÂfor us, let's name it littleballoffur10.sparse6, after one of the fluffy artifacts in the museum. We want to give our visitors a bite-sized piece, so to speak, of this network to explore. This means we need to extract a connected subgraph which, in layman's terms, is just a smaller piece of the puzzle that still holds together and tells a coherent story.

To do this, we'd use something called the MetropolisHastingsRandomWalkSampler. Imagine it's a bit like a guided tour where there's a probability that you'll step from one artifact to the next, but only the most engaging stories, the ones with the strongest connections, make the cut. So, we set out a plan to walk through this network, and each time we encounter a new 'artifact', or node, we decide if it's interesting enough to include it in our tour, ensuring that in the end, the subset of artifacts we've selected is as interesting as it can beÃ¢ÂÂand connected, like a well-thought-out museum trail.

The challenge, as you might have guessed, is to lead this 'guided tour' through our digital graph, sampling as we go, until we've got just the right number of nodes for our targeted educational subgraph. Say we want a connected subgraph of 15 nodes, which in our case are like individual artifacts or stories that need to flow from one to the next.

So our question really boils down to this: how do we use MetropolisHastingsRandomWalkSampler to take a stroll through our graph from littleballoffur10.sparse6 and creatively select a connected story that includes a specific number of 'artifacts'? And we want our path to be random yet selective, much like curating an exhibit that's both intriguing and understandable for our visitors.
### Response:
import networkx as nx
from littleballoffur import MetropolisHastingsRandomWalkSampler
G = nx.read_sparse6('littleballoffur10.sparse6')
sampler = MetropolisHastingsRandomWalkSampler(number_of_nodes=20, seed=42, alpha=0.5)
sampler_G = sampler.sample(G, start_node=None)
print(sampler_G.edges())
print(sampler_G.nodes());
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