<｜begin▁of▁sentence｜>### Instruction:
As a network analyst specializing in community detection algorithms, I can help you with detecting communities in the Power Grid graph representation of a real-world electrical network system. By using the Core Expansion algorithm from the cdlib library, we can identify clusters of highly interconnected nodes within the network. Once the communities are detected, I can provide you with a JSON representation showcasing the structure and composition of these identified groups.

Imagine you are a network analyst working for an energy company responsible for managing a complex electrical grid system. Your task is to analyze the network connectivity and identify tightly-knit groups of substations and power lines that exhibit a high degree of interdependence. By leveraging the Core Expansion algorithm available in the cdlib library, you aim to detect these cohesive communities within the Power Grid graph, allowing you to gain insights into the underlying structure of the network and potentially optimize its efficiency and reliability. Once the community detection process is completed, you plan to visualize the results in a JSON format for further analysis and decision-making within your organization.
### Response:
import networkx as nx
from cdlib import algorithms

# Load the Power Grid graph
g = nx.read_gml('power.gml')

# Apply the Core Expansion community detection algorithm
coms = algorithms.core_expansion(g)

# Get the JSON representation of the detected communities
coms_json = coms.to_json()

print(coms_json)
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