<｜begin▁of▁sentence｜>### Instruction:
As an expert in network analysis using networkx, I can help you with the application of community detection algorithms to understand collaboration patterns in Astrophysics. If you have the Astrophysics collaborations represented as a graph in a GML file named astro-ph.gml, I can guide you through using the Louvain algorithm available in cdlib to identify distinct communities within the network. Once the communities are identified, we can calculate the average fraction of edges of a node that connect it to nodes outside its community, known as the average out degree fraction (avg_odf). This can provide insights into the interconnectedness and relationships among different research groups or individuals in the Astrophysics domain.
### Response:
import networkx as nx
from cdlib import algorithms, evaluation

# Loading the Astrophysics collaborations graph
G = nx.read_gml('astro-ph.gml')

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

# Calculating the average fraction of edges of a node that point outside its community
odf = evaluation.avg_odf(G, communities)

print(f'Astrophysics collaborations graph - Average Out Degree Fraction (avg_odf): {odf}')
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