<｜begin▁of▁sentence｜>### Instruction:
Imagine we're crafting a narrative about a bustling karate club, a microcosm capturing the myriad interactions and nuanced relationships of its members. In this story, we meticulously detail the web of connections that extend beyond the dojo's walls—how each member interacts with the others in their daily lives. These interactions form a rich tapestry, a network of bonds begging to be explored.

Let's now delve deeper into this social saga and unravel the layers of camaraderie and fellowship within the club. As a writer, you'd want your readers to visualize not just the individual interactions but also the distinct social circles that emerge from them. But how do we uncover these hidden alliances and rivalries?

Our quest leads us to the 'karate club.gml' file, which encodes this intricate network of kinships and confrontations. By utilizing the naive greedy modularity maximization method from NetworkX library, a tool for the analytical, we can discern the club’s inner circles. But how do you, as the weaver of this tale, present the emergence of these communities from a seemingly random tangle of relationships? How can this method shed light on the underlying structure of our karate club's social fabric?
### Response:
import networkx as nx

# Create a simple and undirected graph
G = nx.karate_club_graph()

# Find communities in the graph using greedy modularity maximization
communities = nx.community.naive_greedy_modularity_communities(G, resolution=1, weight=None)

# Print the sorted list of nodes in each community
for community in communities:
    print(sorted(community))
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