<｜begin▁of▁sentence｜>### Instruction:
As a meteorologist, you often need to analyze complex data to understand weather patterns and phenomena. Imagine you're tasked with examining a network of atmospheric systems to identify distinct weather patterns. You have access to a specialized tool called "WeatherGraph Analyzer" (WGA), which can import data from various sources, including GML files.

Your current assignment involves analyzing a network of political books, represented as nodes and edges in a GML file named 'polbooks.gml'. Your goal is to detect different "communities" within this network, which could represent distinct clusters of books with similar themes or topics. To accomplish this, you plan to utilize the 'k-cut' algorithm available within the WGA tool, with a specific parameter setting: the maximum value of k set to 4.

Once you've successfully identified these communities, your next step is to present your findings in a format that's easy to understand and share with your colleagues. To achieve this, you'll generate a JSON representation of the detected communities using the output functionality provided by the WGA tool. This JSON file will provide a structured overview of how the political books are grouped into different communities, aiding further analysis and interpretation.
### Response:
import networkx as nx
from cdlib import algorithms

# Read the graph from the GML file
G = nx.read_gml('polbooks.gml')

# Perform the k-cut community detection algorithm on the graph
communities = algorithms.kcut(G, kmax=4)

# Generate a JSON representation of the detected communities
communities_json = communities.to_json()

print(communities_json)
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