<｜begin▁of▁sentence｜>### Instruction:
As a competitive eater participating in a major eating competition, you're keen to analyze the network of coauthorships in the competitive eating community. The data on coauthorships is stored in a file named "coauthorships.gml." You're interested in using the Louvain and Leiden community detection algorithms in the cdlib library to uncover the distinct communities within the network. Additionally, you want to compute the Overlapping Normalized Mutual Information (ONMI) score using the LFK method to assess the similarity between the community structures identified by the two algorithms. This will provide valuable insights into the interconnectedness and overlapping nature of the competitive eating community.
### Response:
import networkx as nx
from cdlib import algorithms, evaluation

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

# Perform community detection using two different algorithms
louvain_communities = algorithms.louvain(G)
leiden_communities = algorithms.leiden(G)

# Compute the Overlapping Normalized Mutual Information between the two community structures
onmi_LFK = evaluation.overlapping_normalized_mutual_information_LFK(louvain_communities, leiden_communities)

print(onmi_LFK.score)
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