<｜begin▁of▁sentence｜>### Instruction:
Hey there! You know, just like how we carefully plan out workout routines to help our clients step by step through their fitness journey Ã¢ÂÂ be it for weight loss, muscle gain, or improving endurance Ã¢ÂÂ in the digital world, data scientists also need to methodically navigate through complex structures. Imagine a network of exercises where each node is a different type of workout, and the connections between nodes represent how one exercise can lead into another based on how they stress similar muscle groups.

Now, just as we might select a subset of exercises to create an effective workout routine for a client with a specific goal, in graph theory, we sometimes want to sample a smaller, manageable subgraph from a larger network to better understand its structure or dynamics. 

Let's say I've got this workout network data stored in a file named 'littleballoffur1.sparse6'. What I'd like to do is use a technique called the DiffusionSampler Ã¢ÂÂ it's a way to simulate how training routines (in our case, "nodes") might spread or 'diffuse' their influence across the network, kinda like how a new fitness trend catches on. My goal is to create a focused routine Ã¢ÂÂ metaphorically speaking Ã¢ÂÂ which in this case is a sampled subgraph with 10 specific exercises (or "nodes"). This would allow me to concentrate on the core aspects of the network, much like focusing on key exercises in a workout plan. How do you reckon we could go about doing that using the DiffusionSampler method?
### Response:
import networkx as nx
from littleballoffur import DiffusionSampler
G = nx.read_sparse6('littleballoffur1.sparse6')
sampler = DiffusionSampler(number_of_nodes=10)
sampler_G = sampler.sample(G)
print(sampler_G.edges(), sampler_G.nodes())
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