<｜begin▁of▁sentence｜>### Instruction:
As a science teacher instructing students in high school, you are conducting a collaborative network analysis project with your students using the High-energy theory collaborations Graph stored in a file named hep-th.gml. 

To delve deeper into the network structure, you decide to utilize the Louvain algorithm available in the cdlib library for community detection. As part of this analysis, you aim to calculate the proportion of individuals in each identified community who have more connections directed externally than internally within their respective communities. This will offer valuable insights into the interconnectedness within these scientific collaborations.

By applying the Louvain algorithm and exploring the network's community structure, you will gain a better understanding of how researchers in high-energy theory collaborations are interconnected and how information flows within and between these communities.
### Response:
import networkx as nx
from cdlib import algorithms, evaluation

# Load the graph
G = nx.read_gml('hep-th.gml')

# Apply a community detection algorithm
communities = algorithms.louvain(G)

# Evaluate the flake_out_degree of the detected communities
flake_out_degree = evaluation.flake_odf(G, communities)

# Print the result
print(flake_out_degree)
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