<｜begin▁of▁sentence｜>### Instruction:
As a property manager, you may need to analyze the power grid network for efficient management of the property's utilities. Suppose you have access to a gml file named power.gml, representing the power grid graph. You can utilize the Louvain method from the cdlib library to perform community detection, which helps identify clusters of interconnected components within the power grid network. Additionally, you can compute the internal edge density of the detected communities, allowing you to assess the strength of connections within each cluster, potentially informing decisions related to infrastructure maintenance and upkeep.
### Response:
import networkx as nx
from cdlib import algorithms, evaluation

# Create a graph from Power grid data
G = nx.read_gml('power.gml')

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

# Compute the internal edge density of the detected communities
internal_edge_density_score = evaluation.internal_edge_density(G, communities)

print(internal_edge_density_score)
<|EOT|>
