<｜begin▁of▁sentence｜>### Instruction:
As an industrial engineer, you've been tasked with optimizing the workflow and operational efficiency of a large research institute where astrophysics collaborations are critical. To help achieve this, you are interested in implementing a community detection analysis on the network of researchers and their collaborations, represented as a graph. The data for the astrophysics collaborations graph is available in a GML file named astro-ph.gml. Your aim is to use the Louvain method for community detection and calculate the link modularity quality function designed for directed graphs with overlapping communities.
### Response:
import networkx as nx
from cdlib import algorithms, evaluation

# Reading the graph
G = nx.read_gml('graph-files/astro-ph.gml')

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

# Computing link modularity
link_modularity_result = evaluation.link_modularity(G, communities)

# Printing the link modularity value
print(link_modularity_result.score)
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