<｜begin▁of▁sentence｜>### Instruction:
As a marriage counselor, imagine you're assisting a couple, John and Sarah, who are struggling with communication in their relationship. John often feels disconnected from Sarah due to their busy schedules and differing interests. To help them understand their relationship dynamics better, you suggest using a network analysis tool called NetworkX. 

You propose analyzing their social network, which represents their interactions and connections with others. You explain that by examining how they are connected within their social circle, they can gain insights into their communication patterns and relationships.

You plan to utilize a graph file named "cond-mat-2005.gml," which contains data about collaborations in the field of condensed matter physics in 2005. This graph reflects how scientists collaborated on research projects during that time.

To delve deeper into their social network, you intend to employ the Louvain community detection algorithm available in the Community Detection Library (cdlib). This algorithm will help identify clusters or communities within their social network, revealing groups of individuals who are more closely connected to each other.

Additionally, you aim to calculate the fraction of individuals within each community who have more connections with people outside their community than within it. This analysis will shed light on whether John and Sarah's interactions primarily occur within their immediate social circles or extend beyond them, mirroring their communication patterns and relationship dynamics.
### Response:
import networkx as nx
from cdlib import algorithms, evaluation

# Create a graph
G = nx.read_gml('cond-mat-2005.gml')

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

# Compute the Flake Out Degree Fraction
flake_odf_score = evaluation.flake_odf(G, communities)

print(flake_odf_score)
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