<｜begin▁of▁sentence｜>### Instruction:
As a Diversity and Inclusion Consultant, imagine you're working with a large energy corporation committed to fostering inclusivity and collaboration among its diverse workforce. In this scenario, you've been tasked with enhancing teamwork and cooperation within the company's engineering department, specifically among teams responsible for managing the power grid infrastructure.

To achieve this goal, you propose utilizing network analysis techniques to uncover natural groupings or communities within the teams. Leveraging the Power Grid Graph data stored in a GML file named "power.gml," you plan to employ the Louvain community detection algorithm available in the `cdlib` library. Additionally, you aim to calculate the link modularity for the identified communities, providing valuable insights into the strength of connections within and between team clusters.

By applying these methods, you hope to facilitate better communication, collaboration, and knowledge sharing among the engineering teams, ultimately contributing to a more cohesive and effective workforce within the organization.
### Response:
import networkx as nx
from cdlib import algorithms, evaluation

# Read the power grid network graph
G = nx.read_gml('power.gml')

# Detect communities in the power grid graph using the Louvain algorithm
communities = algorithms.louvain(G, weight='weight')

# Calculate the link modularity for the detected communities
link_modularity_score = evaluation.link_modularity(G, communities)

print('Link Modularity:', link_modularity_score)
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