<｜begin▁of▁sentence｜>### Instruction:
As a radiologist, I spend my days studying various medical images, interpreting the subtle variations in the scans to diagnose conditions, and recommend the appropriate treatments. My work often involves looking for patterns in the imagery that might indicate a disease, and I rely on my years of training and experience with advanced imaging techniques like MRIs and CT scans to make informed decisions. Images to me are like intricate puzzles, where each piece reveals something about the patient's health.

Now, let's consider a different but related scenario where data visualization and pattern recognition come into playÃ¢ÂÂthis time, involving the analysis of networks and graphs within a computational context rather than medical imaging. Instead of examining an MRI, imagine we're looking at a complex network structure, not unlike the neural pathways in the human brain, encoded in a 'littleballoffur2.sparse6' file format. 

We want to use a specific toolÃ¢ÂÂa GraphReader class provided by the littleballoffur libraryÃ¢ÂÂto bring this network into a more tangible form for analysis. Specifically, our task is to load this file into its rightful representation as a NetworkX graph object. How would we approach this task, allowing us to scrutinize the details of the network just as carefully as we would assess an MRI scan for diagnostic purposes?
### Response:
from littleballoffur.dataset import GraphReader
reader = GraphReader(dataset='wikipedia')
G = reader.get_graph()
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