<｜begin▁of▁sentence｜>### Instruction:
As a dental hygienist, you're familiar with the importance of thorough examination and preventive care to maintain oral health. Similarly, in network science, understanding the connections between researchers is vital for collaboration and knowledge dissemination.

Imagine you're tasked with analyzing the collaborative network among researchers in the field of dentistry, represented as a Coauthorships in network science graph stored in a file called "netscience.gml". You want to identify distinct research communities within this network to better understand the dynamics of collaboration.

To achieve this, you can utilize the Clique Percolation Method (CPM) function provided by the Community Detection Library (cdlib). With a resolution parameter set to 1, you can detect communities based on the strength of connections between researchers.

Once you've identified these communities, your next step is to transform the detected communities into a JSON representation for further analysis and visualization.

By applying this method, you can gain insights into how researchers in dentistry collaborate and form distinct communities, aiding in the advancement of knowledge and innovation in the field.
### Response:
import networkx as nx
from cdlib import algorithms

# Read the coauthorships network science graph
G = nx.read_gml('netscience.gml')

# Perform community detection using the CPM algorithm
communities = algorithms.cpm(G, resolution_parameter=1)

# Get the JSON representation of the detected communities
communities_json = communities.to_json()
print(communities_json)
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