<｜begin▁of▁sentence｜>### Instruction:
As a Photographer who captures and edits images to create visual representations of people, places, or events, imagine you are tasked with analyzing a network of coauthorships in the field of network science. Consider importing the data from the netscience.gml file to identify distinct communities using the Louvain algorithm in the Community Discovery Library (cdlib). Subsequently, calculate the proportion of individuals within a community who have more connections externally than internally in order to gain insights into the network's structure and potential boundary-spanning collaborations.
### Response:
import networkx as nx
from cdlib import algorithms, evaluation

# Read the network science coauthorships graph
G = nx.read_gml('netscience.gml')

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

# Compute the fraction of nodes in S that have fewer edges pointing inside than to the outside of the community
flake_out_degree_fraction = evaluation.flake_odf(G, communities)

print(flake_out_degree_fraction)
<|EOT|>
