<｜begin▁of▁sentence｜>### Instruction:
As an anthropologist, I've been deeply engrossed in the study of interrelations within a particular community, weaving through the intricate tapestry of their social structures, cultures, and shared histories. Observing the intricate networks that bind the individuals to their community, very much like the complex connections one might find in a digital graph, has always been a fascinating endeavor. Recently, I've been intrigued by the potential to analyze these networks through a computational lens, allowing us to visualize and understand the underlying patterns within the society. Utilizing sophisticated tools from network science could shed light on how relationships evolve and influence the social fabric of the group.

With this context in mind, I've come across an interesting graph, almost analogous to a snapshot of the social connections within a tribe, which is stored in a file named 'littleballoffur4.sparse6'. This graph, a digital representation of social connections, could reveal much about the way members of the society interact with each other. In order to focus on a smaller sub-section of this network and understand its structure in a manageable and simplified manner, akin to tracing a family lineage within a tribe, I'm interested in employing the DiffusionTreeSampler to extract a sample.

This sample should reflect a coherent sub-network that maintains its original connections, akin to how a family unit's lineage would show direct descent and inherent relationships. Specifically, I'm looking to obtain an induced treeÃ¢ÂÂa subgraph that's connected and contains no cyclesÃ¢ÂÂwith exactly 10 nodes, which could represent, for example, ten pivotal members of the society whose relationships we want to study intensively.

Can you elucidate how the DiffusionTreeSampler could be used to isolate such a subgraph, extracting a 'family tree' of sorts with 10 nodes from the graph encoded in the 'littleballoffur4.sparse6' file?
### Response:
import networkx as nx
from littleballoffur import DiffusionTreeSampler
G = nx.read_sparse6('littleballoffur4.sparse6')
sampler = DiffusionTreeSampler(number_of_nodes=10)
sampler_G = sampler.sample(G)
print(sampler_G.edges(), sampler_G.nodes())
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