<｜begin▁of▁sentence｜>### Instruction:
As a network analyst, you're working on a project to understand the social dynamics within a large company. Specifically, the 'Les Miserables' social network graph contains valuable data on employee interactions and connections. Using the Louvain method in the cdlib library, you need to perform community detection to identify groups within the company. Additionally, you're interested in computing the cut ratio to understand the fraction of existing edges relative to all possible edges that leave each community. This analysis will provide insights into the division of employees and their connections, potentially leading to more effective team structures and communication strategies within the organization.
### Response:
import networkx as nx
from cdlib import algorithms, evaluation

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

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

# Compute the cut ratio for the detected communities
cut_ratio_value = evaluation.cut_ratio(G, communities)

print(f'Cut ratio of the partition: {cut_ratio_value}')
<|EOT|>
