<｜begin▁of▁sentence｜>### Instruction:
As a faculty advisor in a research institute, you are interested in analyzing the coauthorship network among researchers in the field of network science. You have access to a GML file named "netscience.gml" which contains the coauthorship graph data. You want to understand how to calculate the scaled density of communities detected using the Louvain method and what implications this metric holds for the community structure. This will help you gain insights into the cohesive nature of research communities within the network and potentially aid in identifying influential groups of researchers.
### Response:
import networkx as nx
from cdlib import algorithms, evaluation

# Loading the graph
G = nx.read_gml('paths_to_netscience.gml')

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

# Compute the scaled density of the detected communities
scd = evaluation.scaled_density(G, communities)

# Print the result
print(scd)
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