<｜begin▁of▁sentence｜>### Instruction:
Right, let's put on our strategic thinking caps! Imagine we're in the thick of it with a client, a fast-growing tech firm. Their network is booming Ã¢ÂÂ that is, their business connections, partnerships, complexity Ã¢ÂÂ you name it. As their trusted business consultant, we've been zeroing in on their growth strategy, dissecting their business ties to unearth potential goldmines of efficiency and untapped market areas. 

We've got their relationship data - it's a sprawling web of interactions and connections mapped out in a graph. But here's the kicker, we're not going to boil the ocean by analyzing the entire network in one go. That's just not practical nor efficient.

So, what I suggest we do is this: we tackle this complex graph systematically. We use a sharp tool from the world of network analysis like a fine scalpel. I'm talking about a randomized technique called CirculatedNeighborsRandomWalkSampler. This nifty method is going to let us zone in on the crux of this network, giving us a digestible subgraph which will spotlight where the core action is happening.

And because we need something tangible, let's zoom in on a subset that's representative but manageable - say a subgraph with precisely 10 nodes. This will give us the insights we need without getting lost in the weeds.

So, circling back to our main point of action: Could you employ the CirculatedNeighborsRandomWalkSampler to extract a 10-node subgraph from the original graph, which we have stored as 'littleballoffur14.sparse6'? We need to roll this data into a format we can actually digest, a GML perhaps, to dissect their strategic position. Let's call this GML file 'strategic_network_snapshot.gml'. It's strategic insights like these that could give our client that killer edge.
### Response:
import networkx as nx
from littleballoffur import CirculatedNeighborsRandomWalkSampler
G = nx.read_sparse6('littleballoffur14.sparse6')
sampler = CirculatedNeighborsRandomWalkSampler(number_of_nodes=10, seed=42)
sampler_G = sampler.sample(G)
print(sampler_G.edges(), sampler_G.nodes())
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