<｜begin▁of▁sentence｜>### Instruction:
As an AI ethicist, my days are often filled with deep contemplation and debate over the moral intricacies of advanced technologies. I continually assess how artificial intelligence influences human decision-making, privacy, and our broader societal norms. Today, I'm examining how algorithms might unintentionally amplify biases, particularly those utilized in network analysis. Our current project involves using graph sampling techniques, yet we must be mindful to ensure the sample represents the original data without introducing new ethical dilemmas.

In this context of ethical scrutiny, we must approach our computational task with diligence. The task at hand involves a graph that we presently have in a specific file format, 'littleballoffur10.sparse6'. We plan to utilize a sampling algorithm, specifically the `RandomEdgeSamplerWithInduction` within the littleballoffur framework. This method isn't just about extracting a subset of connections but also about preserving the network's structure by including edges that maintain the relationships among sampled nodes, akin to considering the broader societal context when evaluating AI implications.

To reframe my research question while retaining its key meaning and applying it to our technical scenario: 
Could you illustrate how to employ the `RandomEdgeSamplerWithInduction` algorithm to extract a representative subgraph with induction from the original network, preserved in 'littleballoffur10.gml'?
### Response:
import networkx as nx
from littleballoffur import RandomEdgeSamplerWithInduction
G = nx.read_sparse6('littleballoffur10.sparse6')
sampler = RandomEdgeSamplerWithInduction(number_of_edges=50, seed=42)
sampler_G = sampler.sample(G)
print(sampler_G.edges())
print(sampler_G.nodes());
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