<｜begin▁of▁sentence｜>### Instruction:
Hey there! So imagine you're on the sales floor, closing deals left and right with your charm and awesome products. Now, you've got this nifty little simulation that's kind of like your client network, right? It's complex, and you're looking to understand the dynamics of a smaller, more manageable cluster within that network. You'd start by identifying key nodes Ã¢ÂÂ like those hot leads or big spenders Ã¢ÂÂ to see how they're interconnected.

Now, let's take that same concept and apply it to a digital graph we've got stored in 'littleballoffur14.sparse6'. You want to zero in on just a portion of this vast network Ã¢ÂÂ say, a subgraph with precisely 10 nodes Ã¢ÂÂ to analyze the flow and connectivity more closely. It's like cherry-picking a small, elite group of clients from your larger database to focus your sales strategy.

To do this, we're going to use the nifty tool called the DiffusionSampler. It's a smart way to simulate the process of referrals and word-of-mouth in the network, kind of like how a juicy deal spreads among clients. This way, we'll extract a representative chunk of our graph that we can study in detail, just like tailoring a pitch for a premium batch of leads.

So, how about I show you how to pull a 10-node subgraph from our main graph using this DiffusionSampler? Think of it as picking out the 10 most connected people at a networking event, the ones who are going to give you the best bang for your sales buck. Ready to dive in?
### Response:
import networkx as nx
from littleballoffur import DiffusionSampler
G = nx.read_sparse6('littleballoffur14.sparse6')
sampler = DiffusionSampler(number_of_nodes=10)
sampled_G = sampler.sample(G)
print(sampled_G.edges(), sampled_G.nodes())
<|EOT|>
