<｜begin▁of▁sentence｜>### Instruction:
As a coffee roaster, you understand the importance of precision and quality in every batch of coffee beans you roast. Imagine you've just received a special shipment of coffee beans from different regions around the world. Each type of bean contributes to the unique flavor profile of your blends, much like characters in a novel contribute to its storyline.

To optimize your blending process and ensure that each batch of coffee has a well-balanced flavor profile, you decide to analyze the interactions between these beans, akin to characters interacting in a story. You recall hearing about a powerful tool called CONGA, which can identify overlapping communities within complex networks. In your case, you want to apply this algorithm to the network of coffee bean interactions, represented in a GML file named "coffeeworld.gml".

Your goal is to detect three distinct communities within this network, each representing a cohesive group of beans that interact closely with each other. Once you've identified these communities, you plan to map them out in a JSON format, providing a clear representation of how the beans are interconnected within each community.

By leveraging the CONGA algorithm and visualizing the community structure, you aim to enhance your understanding of how different types of coffee beans complement each other, ultimately refining your blending process to create exceptional coffee blends with harmonious flavors.
### Response:
import networkx as nx
from cdlib import algorithms

# Read the graph
G = nx.read_gml('lesmis.gml')

# Apply CONGA algorithm to detect overlapping communities
# with the desired number of communities set to 3
communities = algorithms.conga(G, number_communities=3)

# Convert the communities to JSON representation
communities_json = communities.to_json()

print(communities_json)
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