<｜begin▁of▁sentence｜>### Instruction:
As a freight forwarder, imagine you are working with a company that handles shipments for multiple clients. You have a network graph representing collaborations between astrophysics researchers, and you have it stored in a GML file named 'astro-ph.gml'. Your task is to use the Louvain and Leiden community detection algorithms provided by the cdlib library to identify distinct communities within the collaboration network. Once you have the node partitionings, you should calculate the Variation of Information to compare the results of the two algorithms. This comparison will help assess the effectiveness of the different algorithms in identifying cohesive research communities within the astrophysics network.
### Response:
import networkx as nx
from cdlib import algorithms, evaluation

# Create a graph from the Astrophysics collaborations dataset
G = nx.read_gml('astro-ph.gml')

# Applying community detection algorithms to the graph
louvain_communities = algorithms.louvain(G)
leiden_communities = algorithms.leiden(G)

# Evaluate the similarity of the two community structures using Variation of Information
voi = evaluation.variation_of_information(louvain_communities, leiden_communities)

print(voi.score)
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