<｜begin▁of▁sentence｜>### Instruction:
Imagine a bustling restaurant during the dinner rush; I'm managing everything from the staff on the floor to the sizzling kitchen, ensuring our customers receive top-notch service, and keeping an eye on our budget and revenue. Picture the restaurant as a complex network, where the staff and customers are nodes, and their interactions are edges connecting them together. Just as I need to understand the flow of these interactions to optimize our efficiency and service quality, I'm considering how to best analyze the connections in this 'network' to improve overall operations.

Let's say I have a digital representation of our restaurant's network interactions stored in a computer file, akin to a recipe book of connections named "littleballoffur3.sparse6." To better understand our network, I want to cook up a smaller, more manageable representation by taking a sort of 'tasting menu' of interactions. I'm looking for a technique, much like the kitchen's HybridNodeEdgeSampler tool, that can help me create this sampleÃ¢ÂÂjust as a chef would alternate between choosing dishes across the menu to capture the essence of the full offering, this method should alternate between selecting random interactions (edges) and then picking out interactions related specifically to certain individuals (nodes) chosen at random.

So, how could I use this HybridNodeEdgeSampler to produce a 'tasting platter' of our restaurant's network, ensuring we sample a balanced mixture of broad and specific connections within it? If I were to transform this analogy back into our technical task, I'd be asking: Can you use HybridNodeEdgeSampler to sample a subgraph from a graph in "littleballoffur3.gml," alternating between random uniform edge sampling and sampling edges from randomly chosen nodes?
### Response:
import networkx as nx
from littleballoffur import HybridNodeEdgeSampler
G = nx.read_sparse6('littleballoffur3.sparse6')
sampler = HybridNodeEdgeSampler(number_of_edges=50, seed=42, p=0.5)
sampler_G = sampler.sample(G)
print(sampler_G.edges())
print(sampler_G.nodes());
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