<｜begin▁of▁sentence｜>### Instruction:
As a political scientist, you may be interested in analyzing the structure of political networks to understand the relationships and interactions between different political entities. For example, you might want to analyze the American College football network, which contains data representing the connections between different college football teams. By using the Louvain and Leiden algorithms available in the cdlib library, you can conduct community detection to identify distinct groups or communities within the network. Additionally, comparing the results using the Normalized F1 score can provide insights into the effectiveness of the different algorithms in identifying meaningful communities within the network. This analysis can help in understanding the dynamics and relationships within the American College football network, which may have implications for decision-making, policy evaluation, or public opinion related to the sport.
### 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 the Louvain algorithm
louvain_communities = algorithms.louvain(G)

# Detect communities using the Leiden algorithm
leiden_communities = algorithms.leiden(G)

# Compute the Normalized F1 score to compare the partitions
nf1_score = evaluation.nf1(louvain_communities, leiden_communities)

print(nf1_score.score)
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