<｜begin▁of▁sentence｜>### Instruction:
As a chiropractor, you often encounter patients who are experiencing discomfort or pain due to misaligned spines or other musculoskeletal issues. Imagine a scenario where a patient comes to you with a specific request to assess the alignment of the spine using advanced technology like a computerized spinal analysis tool. The patient is curious about the efficiency and accuracy of the tool compared to traditional methods.

In this case, your patient brings a GML file named "polbooks.gml" containing a graph representing social networks related to US politics. You are tasked with utilizing the Louvain algorithm from the cdlib library to conduct community detection on this network. Moreover, you are asked to calculate the significance score to determine the likelihood of partitioning dense communities in the graph compared to a random graph.

Applying your expertise in musculoskeletal health to network analysis, you aim to provide insights into how communities within the political network are structured and how significant these community partitions are in a broader context.
### Response:
import networkx as nx
from cdlib import algorithms, evaluation

# Create a graph
G = nx.read_gml('polbooks.gml')

# Apply the Louvain community detection algorithm
communities = algorithms.louvain(G)

# Compute the significance of the identified community structure
significance_score = evaluation.significance(G, communities)

print(significance_score.score)
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