<｜begin▁of▁sentence｜>### Instruction:
As a Health Educator, imagine that you are working on a project to analyze the social networks and community structures within a local neighborhood to better understand how residents interact and engage with health education programs. You have access to a dataset called "football.gml," which contains network data representing the connections between American college football teams. Your goal is to use the Louvain and Leiden community detection methods in the cdlib library to identify distinct communities within the football network. Additionally, you need to compare the results of the two methods using the Normalized Mutual Information metric to assess the similarity of the community detection outcomes. This will help you evaluate the reliability and consistency of the community structure identification methods and their potential application in understanding community engagement with health education initiatives.
### Response:
import networkx as nx
from cdlib import algorithms, evaluation

# Load the American college football network
G = nx.read_gml('football.gml')

# Detect communities using two different algorithms
louvain_communities = algorithms.louvain(G)
leiden_communities = algorithms.leiden(G)

# Compute the Normalized Mutual Information between the two partitions
nmi_result = evaluation.normalized_mutual_information(louvain_communities, leiden_communities)

print(nmi_result.score)
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