<｜begin▁of▁sentence｜>### Instruction:
As an expert in network analysis, I often work on projects involving the exploration of coauthorship relationships among researchers in the field of network science. I frequently use networkx for analyzing such relationships.

In this instance, I am looking to apply the Louvain community detection algorithm to the coauthorship network graph of network science researchers. The graph data is stored in a gml file named `netscience.gml`.

My goal is to utilize the Louvain community detection algorithm from the `cdlib` library to identify distinct clusters or communities within the network based on coauthorship patterns. Once I run the algorithm, I aim to obtain a JSON representation of the resulting node clustering to better understand the structure of the network and the relationships between researchers.

By using this approach, I hope to gain insights into the collaborative dynamics within the network science community and uncover any hidden patterns or subgroups that exist among the researchers.
### Response:
import networkx as nx
from cdlib import algorithms

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

# Apply Louvain community detection algorithm
communities = algorithms.louvain(G, weight='weight', resolution=1.0)

# Convert the communities to JSON format
communities_json = communities.to_json()

print(communities_json)
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