<｜begin▁of▁sentence｜>### Instruction:
As a Criminal Psychologist who delves into understanding criminal behavior, motivations, and psychological factors in legal contexts, you're often tasked with analyzing intricate networks of interactions within criminal organizations. Imagine you're investigating a notorious gang, code-named "Les Misérables," known for its complex social structure. To gain insights into their internal dynamics, you've obtained a digital representation of their network, stored in a file named "lesmis.gml."

Now, your task involves utilizing advanced computational tools to uncover the underlying community structures within this network. Specifically, you're interested in employing a function called `significance_communities`, provided by the Community Detection Library (cdlib), to detect significant communities within the Les Misérables network. Additionally, you aim to compute the significance scores associated with each detected community, aiding in understanding the hierarchy and cohesion within the gang's different factions.
### Response:
import networkx as nx
from cdlib import algorithms

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

# Perform community detection using the significance communities algorithm
communities = algorithms.significance_communities(G)

# Calculate the significance score
significance_score = communities.significance()

print(f'The significance score of the detected communities is: {significance_score}')
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