<｜begin▁of▁sentence｜>### Instruction:
As a speech-language pathologist, you may encounter complex networks related to patient communication and cognitive health, such as interconnected speech and language patterns among individuals. To analyze these networks, you can leverage the PageRankBasedSampler from NetworkX, a Python library for analyzing complex networks. For example, if you have a network with 100 nodes representing different communication or cognitive elements and 500 edges representing the relationships between them, you might want to sample 50 nodes based on their PageRank score to focus on the most influential elements. In this scenario, you can use the PageRankBasedSampler API to perform this sampling and obtain a smaller subset of nodes based on their importance in the network. This approach can help in prioritizing interventions, exercises, or strategies tailored to the most impactful elements within the network.
### Response:
import networkx as nx
from littleballoffur import PageRankBasedSampler

G = nx.gnm_random_graph(100, 500)

sampler = PageRankBasedSampler(50)
sampled_G = sampler.sample(G)

print(sampled_G.nodes())
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