<｜begin▁of▁sentence｜>### Instruction:
As a research assistant, let's consider a scenario where we are assisting a team of social scientists studying political blog interactions. They have collected data on the connections between political blogs and want to perform community detection to identify groups of blogs with similar interests or affiliations. They are interested in using networkx to analyze the data, which is available in a GML file named 'polblogs.gml'. The team would like to compare the results of community detection using both the eigenvector and label propagation methods, and then calculate the Variation of Information to assess the differences in the resulting community partitioning.
### Response:
import networkx as nx
from cdlib import algorithms, evaluation

# Load the political blogs graph
G = nx.read_gml('polblogs.gml')

# Perform community detection using two different algorithms
eigenvector_communities = algorithms.eigenvector(G)
label_propagation_communities = algorithms.label_propagation(G)

# Calculate the Variation of Information between the two partitionings
voi = evaluation.variation_of_information(eigenvector_communities, label_propagation_communities)

print(voi.score)
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