<｜begin▁of▁sentence｜>### Instruction:
As a professional event planner, you may come across situations where you need to analyze the effectiveness of different strategies to bring people together for specific purposes. Imagine you are organizing a large conference which will bring together people from diverse political backgrounds. You have access to a dataset of political blog networks in a GML file named polblogs.gml. In order to assess the effectiveness of different community detection algorithms in understanding the network structure, you need to use the Louvain and Leiden community detection algorithms provided by the cdlib library to perform community detection on the political blog network graph. Subsequently, you are required to compute the Normalized F1 score to assess the similarity between the two community partitions obtained. This will help you understand the cohesion and division within the political blog network, ensuring that the event is a success and provides value to participants.
### Response:
import networkx as nx
from cdlib import algorithms, evaluation

# Read a graph from the Political blogs dataset
G = nx.read_gml('polblogs.gml')

# Detect communities using the Louvain method
louvain_communities = algorithms.louvain(G)

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

# Evaluate the similarity of the two community detections using Normalized F1 score
nf1_score = evaluation.nf1(louvain_communities, leiden_communities)

# Output the Normalized F1 score
print(nf1_score.score)
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