<｜begin▁of▁sentence｜>### Instruction:
As a jazz musician, you might be interested in analyzing the connections between different jazz music styles and their relationships within a network. Imagine you have a collection of jazz music styles and their adjacencies represented as a graph in a GML file called adjnoun.gml. To better understand the dynamics within this network, you could use the Louvain algorithm in the cdlib library to detect communities of closely related music styles. Once the communities are identified, you could then calculate the flake out degree fraction of each community to gain insights into the interconnectedness of different jazz music styles within the network.
### Response:
import networkx as nx
from cdlib import algorithms, evaluation

# Reading the graph structure
G = nx.read_gml('adjnoun.gml')

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

# Computing the flake_out degree fraction
flake_odf_score = evaluation.flake_odf(G, communities)

# Printing the score
print(flake_odf_score)
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