<｜begin▁of▁sentence｜>### Instruction:
As a Freight Forwarder coordinating international shipments for various businesses, you receive a request from a research institute specializing in high-energy physics. They've provided a GML file named "hep-th.gml" containing a network representation of their collaboration graph. They seek assistance in analyzing the community structure within this network to better understand the dynamics of their research collaborations.

To fulfill their request, you decide to utilize the Louvain community detection algorithm, available through the Community Detection Library (cdlib). After partitioning the collaboration network into communities, you aim to compute the Z-modularity score, a metric that evaluates the quality of the detected community structure.

Your task involves implementing the Louvain function from cdlib to perform community detection on the provided collaboration graph. Subsequently, you'll calculate the Z-modularity score to assess the effectiveness of the detected community structure. Finally, you'll provide the research institute with the Z-modularity score as part of your analysis report, aiding them in gaining insights into their collaboration patterns.
### Response:
from cdlib import algorithms
import cdlib
import networkx as nx

# Read the graph from the provided gml file
G = nx.read_gml('hep-th.gml')

# Apply the Louvain algorithm for community detection
communities = algorithms.louvain(G)

# Compute the Z-modularity for the detected communities
z_modularity = cdlib.evaluation.z_modularity(G, communities)

# Print the Z-modularity value
print("Z-Modularity:", z_modularity.score)
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