<｜begin▁of▁sentence｜>### Instruction:
As a fashion blogger, you may want to compare different community detection results obtained from analyzing trends and styles. Imagine you have data from various fashion influencers and you want to analyze the communities within these influencers' networks. You can compare the results obtained using different algorithms such as the eigenvector and Leiden algorithms. You're interested in calculating the Adjusted Mutual Information metric to compare these results.
### Response:
import networkx as nx
from cdlib import algorithms, evaluation

# Load the Internet graph
G = nx.read_gml('as-22july06.gml')

# Perform community detection using two different algorithms
eigenvector_communities = algorithms.eigenvector(G)
leiden_communities = algorithms.leiden(G)

# Compute the adjusted mutual information between the partitions
ami = evaluation.adjusted_mutual_information(eigenvector_communities, leiden_communities)

print(ami.score)
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