<｜begin▁of▁sentence｜>### Instruction:
As a Marriage Counselor, my expertise lies in fostering understanding and harmony between couples facing relationship challenges. Imagine you and your partner are like nodes in a complex social network, each with unique connections and influences. Now, let's apply this analogy to a scenario involving political books.

Consider a situation where you and your partner are avid readers with diverse political interests. You've amassed a collection of political books, each representing a different perspective or ideology. To navigate this intellectual landscape, you decide to analyze your library using a tool called the "Book Network Analyzer" (BNA) API.

Using the BNA API, you can input data from your collection, stored in a Graph Markup Language (GML) file named "polbooks.gml." This file maps out the connections between various political books based on shared themes, authors, or readership.

Now, let's say you want to understand the dynamics surrounding a specific book, let's call it "The Political Compass" (represented by node 0 in the network). Similar to how a counselor might delve into a particular issue affecting a couple, you wish to explore the community of books surrounding "The Political Compass" using a method called the LSWL community detection algorithm.

Once you've applied the LSWL algorithm via the BNA API, you'll obtain insights into the cohesive clusters or "communities" within your political book network. These communities reflect groups of books that share significant thematic or ideological similarities. To facilitate further analysis and discussion, you can then generate and print a JSON representation of these detected communities, providing a structured overview of the diverse political ideologies present in your book collection.
### Response:
import networkx as nx
from cdlib import algorithms

# Load the US political books graph
G = nx.read_gml('polbooks.gml')

# Apply LSWL community detection algorithm
coms = algorithms.lswl(G, query_node=0)

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