<｜begin▁of▁sentence｜>### Instruction:
As a Materials Engineer, you spend countless hours analyzing various materials and compounds, tinkering with their properties to innovate and enhance their applications within different sectors. One day, you're examining the intricate network of molecules in a composite material, considering how the bonds and connections between them contribute to the overall strength and performance of the material. This network is analogous to a vast web of interconnected nodes, akin to a graph in the computational sense. Your mind drifts to a computational approach to analyze this web: What if you could simulate the molecular network as a graph and extract a simplified representation through various paths to better understand the underlying structure?

You remember hearing about graph sampling in the context of data science and wonder if it could be applied here. Specifically, you consider using an algorithm to explore the network by identifying random shortest paths between molecule pairsÃ¢ÂÂthe nodes in your graph analogyÃ¢ÂÂto sample a representative sub-structure that maintains the core characteristics of the entire network.

Translating this into your computational task, you'd like to apply a method known as the ShortestPathSampler from the littleballoffur package to a graph representation of the material's molecular network. The objective is to extract a subgraph that contains a predetermined number of nodes, thereby providing a simplified yet informative snapshot of the material's structure.

Could you use the ShortestPathSampler to draw a subgraph with an exact count of nodes by tracing random shortest paths between node pairs in my existing graph, which, by the way, is encapsulated in a graph file? And for the sake of precision in this computational experiment, assume that the original graph is available in the file named 'littleballoffur11.gml'.
### Response:
import networkx as nx
from littleballoffur import ShortestPathSampler
G = nx.read_sparse6('littleballoffur11.sparse6')
sampler = ShortestPathSampler(number_of_nodes=10)
sampler_G = sampler.sample(G)
print(sampler_G.edges(), sampler_G.nodes())
<|EOT|>
