<｜begin▁of▁sentence｜>### Instruction:
Alright, let's imagine I'm an RN and I'm walking you through a scenario that's a bit more familiar to my daily work environment. When we assess patients, we often consider numerous factors that relate to their health outcomes, such as their medical history, vital signs, or symptoms. This process, in many ways, is similar to measuring the centrality of nodes in a network - it's all about figuring out which elements are most critical in the system.

Now, think of our patient network as a complex web, a graph if we're getting technical. Just as some patients require more attention due to their condition's severity, some nodes in our graph hold more weight because they're more centrally connected. In the same way we might focus resources on our high-need patients, we want to sample a subgraph with nodes that represent the backbone of the entire network, in proportion to their "health" - or in graph terms, their degree centrality.

So, with this in mind, let's reframe our initial inquiry. Using the file named 'littleballoffur14.gml', how could we employ a DegreeBasedSampler to extract a representative subgraph? We want this subset to mirror the essence of the original graph, similar to the way we'd prioritize care for our patients, focusing on those who play a pivotal role in the health of the community. Now, let's get to work on applying this DegreeBasedSampler to effectively manage our 'patient load', or nodes, in this graph.
### Response:
import networkx as nx
from littleballoffur import DegreeBasedSampler
G = nx.read_sparse6('littleballoffur14.sparse6')
sampler = DegreeBasedSampler(number_of_nodes=10, seed=42)
sampler_G = sampler.sample(G)
print(sampler_G.edges(), sampler_G.nodes())
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