<｜begin▁of▁sentence｜>### Instruction:
As a network analysis expert, I can help you with the community detection and analysis of the High-energy theory collaborations graph. Imagine you have been asked to analyze a large dataset that includes information about collaborations between various high-energy physicists. Your task is to use the networkx library for Python to load the dataset from a GML file named hep-th.gml. Once you have the graph data loaded, you need to apply the Louvain method from the cdlib library to perform community detection on the graph. After detecting the communities, you are interested in computing the maximum fraction of edges of a node within a community that point outside the community itself. This analysis will provide insights into the interconnectedness of different research groups within the high-energy physics community.
### Response:
import networkx as nx
from cdlib import algorithms, evaluation

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

# Detect communities using the Louvain method
communities = algorithms.louvain(G)

# Compute the maximum fraction of edges of a node in communities that point outside the community itself
max_out_degree_fraction = evaluation.max_odf(G, communities)

print(max_out_degree_fraction.score)
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