<｜begin▁of▁sentence｜>### Instruction:
Imagine I'm starting my day at the school, planning a new way to explore how students interact within their different social circles, a vital aspect of understanding the social dynamics at play within the school environment. My goal here is to map out these relational networks and uncover potential patterns that could inform our interventions. Now, one approach I've found intriguing involves utilizing the concept of a social graph where the nodes represent students, and the edges represent their interactions.

To bring this theory to life, I've gotten my hands on some anonymized data about student interactions, which has been encoded into a graph file named 'littleballoffur7.gml'. Fascinatingly, it's possible to use graph sampling methods that consider not just direct connections but the shared friendships these students have Ã¢ÂÂ essentially the friends of their friends. This 'common neighbor awareness' could give us deeper insights into the tight-knit clusters within our social graph.

Okay, so here's what I'm grappling with: How can I, using a CommonNeighborAwareRandomWalkSampler, create a smaller, representative snapshot of this social graph? What I need is a subset that maintains the essence of our larger data set, with a particular focus on those common neighbor relationships. To maintain manageability, let's say this subgraph should contain a specific number of nodes. Can you walk me through how this could be done, keeping in mind the sophisticated complexity our student relationships exhibit? If the data was initially in a different format, we would of course need to convert it into the 'littleballoffur7.gml' file prior to sampling.
### Response:
import networkx as nx
from littleballoffur import CommonNeighborAwareRandomWalkSampler
G = nx.read_sparse6('littleballoffur7.sparse6')
sampler = CommonNeighborAwareRandomWalkSampler(number_of_nodes=10)
sampler_G = sampler.sample(G)
print(sampler_G.edges(), sampler_G.nodes())
<|EOT|>
