<｜begin▁of▁sentence｜>### Instruction:
Alright, let's take a moment to consider the dynamic tapestry of intercultural communication, where every interaction weaves a unique pattern, reflecting the diverse threads of our global community. Similarly, in the realm of graph theory, each network encapsulates a complex interplay of connections, akin to the intricate relationships among the individuals from various cultural backgrounds that we explore in our trainings.

Imagine that we're working with an organization that's like a social network graph - a system of interconnected employees from different corners of the world. This network is not just a theoretical concept; it has its own unique structure and intricacies, captured in a data file, much like the nuanced profiles we create for our training sessions to understand the cultural complexities of organizational teams.

Now, suppose we have this organizational network data saved in a 'littleballoffur5.sparse6' file, symbolizing the entanglements of interactions within a company. We're interested in examining a small, representative subgroup of this network - a microcosm that reflects the broader interplay of cross-cultural connections within the organization.

To extract this focused view, much like isolating a key scenario in a training workshop to scrutinize and learn from, we intend to employ a technique similar to FrontierSampler, a method used in graph theory to capture a subset of nodes that maintains the structural characteristics of the original network. This approach helps us to understand the subtleties in a manageable context.

In concrete terms, how would we navigate using the FrontierSampler to obtain a subgraph with exactly 10 nodes from our 'littleballoffur5.sparse6' file, ensuring that our sample portrays the interconnectedness of our global team within the organization?
### Response:
import networkx as nx
from littleballoffur.exploration_sampling import FrontierSampler
G = nx.read_sparse6('littleballoffur5.sparse6')
sampler = FrontierSampler(number_of_nodes=10)
sampler_G = sampler.sample(G)
print(sampler_G.edges(), sampler_G.nodes())
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