<｜begin▁of▁sentence｜>### Instruction:
As we delve into the intricate social structures of the karate club network, a dataset intriguingly nuanced for the study of community dynamics, we aim to apply a computational technique that mirrors our search for natural divisions within this social ecosystem. Our objective is to employ the greedy modularity maximization algorithm — an elegant, yet computationally parsimonious method well-suited for discerning the densely knit communities that undoubtedly proliferate within the network's fabric.

The network in question, encapsulated within the "karate_club_graph" function provided by the NetworkX library, offers a fertile ground for testing our hypothesis that social entities self-organize into discernible clusters. Once we have executed the algorithm, our goal is to meticulously catalog the members of each emergent community. We shall then proceed to curate a list, in ascending order, of the nodes that comprise every identified community, thus imposing a structured taxonomy on what was previously a conglomeration of interconnected individuals.

By undertaking this analysis, we're not merely executing a computational task; we're peering into a microcosm of society, examining how individuals coalesce into communities. Could you guide me through the process of how to transmute this raw network data into a delineated map of its inherent community structure using NetworkX's tools?
### Response:
import networkx as nx

# Create a graph
G = nx.karate_club_graph()

# Find communities in the graph
communities = nx.community.greedy_modularity_communities(G, weight='weight', resolution=1, cutoff=1, best_n=None)

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