<｜begin▁of▁sentence｜>### Instruction:
As a museum curator organizing an exhibition on contemporary art, I'm looking to gain insights into the High-energy theory collaborations within the artist community. I have obtained a network graph detailing these collaborations as a GML file named hep-th.gml. I'd like to use the Louvain method in the `cdlib` library to identify the different artist communities, and then compute the modularity density of these communities to understand their internal structure and clustering. Can you assist me with this?
### Response:
import networkx as nx
from cdlib import algorithms, evaluation

# Read the high-energy theory collaborations graph
G = nx.read_gml('hep-th.gml')

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

# Compute the modularity density
modularity_density = evaluation.modularity_density(G, communities)

# Print the modularity density score
print(modularity_density.score)
<|EOT|>
