<｜begin▁of▁sentence｜>### Instruction:
Alright, picture this: It's another busy week at school, and in our computer science class, we've just embarked on a fascinating journey into the world of graph theory. Everyone is abuzz with excitement as I unveil the week's project Ã¢ÂÂ diving into complex networks with the help of some graph analysis tools. The students, with their minds hungry for knowledge, are eager to put theory into practice.

We've talked about the topography of networks, how densely knit they are, and how even in this intricate web, there can be patterns and insights just waiting to be discovered. We come across the littleballoffur package, a curious tool that'll help us peel the layers of our network data. The enthusiasm is palpable as we take the graph read from our file, "littleballoffur9.sparse6", and consider the challenge at hand.

So, here's what we're going to tackle today, class Ã¢ÂÂ We have this dense graph, and we want to simplify it a bit to study its structure in a more digestible form. The goal is to take a representative sample of this complex network. Specifically, we're going to use the RandomEdgeSampler tool to snatch a smaller subgraph, but we want this snippet to consist of exactly 30 edges to keep things neat and comprehensible.

Now, if our file format was different, say a gml instead of a sparse6, we'd obviously reference that as needed, but the essence of the challenge would stay the same. The question we're exploring is: How can we use RandomEdgeSampler to extract a subgraph of 30 edges from our original graph encoded in "littleballoffur9.sparse6"? Let's work through this together, and perhaps, just like detectives, we'll uncover some secrets hidden within the connections of this network.
### Response:
import networkx as nx
from littleballoffur import RandomEdgeSampler
G = nx.read_sparse6('littleballoffur9.sparse6')
sampler = RandomEdgeSampler(number_of_edges=30, seed=42)
sampler_G = sampler.sample(G)
print(sampler_G.edges(), sampler_G.nodes())
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