<｜begin▁of▁sentence｜>### Instruction:
As an orthodontist, you diagnose, prevent, and correct dental and facial irregularities like malocclusions or misalignments. This involves using braces, aligners, retainers, or other orthodontic treatments to improve dental aesthetics, function, and health, as well as enhance facial harmony. You typically provide orthodontic evaluations, consultations, and treatments in orthodontic practices, clinics, or hospitals.

Now, let's apply this to your scenario. Suppose you're a researcher studying community structures in a network of books about US politics. Just as an orthodontist diagnoses and treats dental issues, you aim to analyze and understand the structure of this network using specialized tools. In this case, you want to use the Louvain method from the Community Detection Library (cdlib) to identify communities within the network of political books. Additionally, you're interested in calculating and printing the overlapping modularity to assess the strength of these community structures. This process parallels the careful examination and treatment planning an orthodontist undertakes to address dental irregularities and enhance overall oral health.
### Response:
import networkx as nx
from cdlib import algorithms, evaluation

# Read the graph
g = nx.read_gml('polbooks.gml')

# Perform community detection using the Louvain method
communities = algorithms.louvain(g)

# Calculate the Overlapping Modularity
modularity_overlap = evaluation.modularity_overlap(g, communities)

# Display the Overlapping Modularity
print(modularity_overlap)
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