<｜begin▁of▁sentence｜>### Instruction:
As a journalist tirelessly chasing down the latest scoop, I understand that data is at the heart of any compelling story. Just recently, I delved into the intricate web of connections shaping the narratives on Wikipedia. It's a fascinating repository, ripe for analysis, which could reveal how information interlinks across countless subjects.

In the data-driven world we navigate, tools are essential Ã¢ÂÂ just like my trusty notepad and recorder for interviews. This time, however, my tools are digital. I came across this software package, littleballoffur, which could be the key to unlocking the stories hidden within the complex networks of data.

I'm looking to take the 'wikipedia' dataset, which I presume is structured to highlight the interconnected nature of topics, editors, or articles, and to dissect it with the precision of a seasoned investigator. For that, I need to convert it into a format I can work with Ã¢ÂÂ a NetworkX graph object is perfect, quintessential for the network analyses I'm aiming to perform. It's akin to translating a witness statement into a narrative that fits into the bigger picture of my investigation.

So, here's what I need to crack the case wide open: How can I employ a GraphReader to extract this treasure trove of information from the 'wikipedia' dataset? And if this dataset comes from a .gml file, I would certainly need to know its name to get my hands on the precise data for my article. Can you help me out?
### Response:
from littleballoffur import GraphReader
reader = GraphReader(dataset='wikipedia')
G = reader.get_graph();
<|EOT|>
