<｜begin▁of▁sentence｜>### Instruction:
As a network analyst, suppose you are working with a team of researchers studying collaboration patterns in network science. You have obtained a dataset representing coauthorship relationships among researchers in the field, stored in the 'netscience.gml' file. Your team needs to identify distinct research communities within this network to better understand how knowledge is shared among scholars in the field.

To achieve this, you are tasked with applying community detection algorithms to the coauthorship network. Specifically, you should utilize the Louvain and Leiden algorithms available in the cdlib library to partition the network into cohesive research communities. Once the partitions are obtained, your team wants to quantitatively assess the similarity between the community structures produced by these algorithms.

To measure the agreement between the partitions, you are required to compute the Overlapping Normalized Mutual Information (ONMI) score using the McDaid et al. method. This metric will help evaluate how well the community detection results from Louvain and Leiden align with each other, shedding light on the underlying collaboration dynamics in the network.

Your expertise in network analysis and proficiency with tools like cdlib are essential in uncovering meaningful insights from the coauthorship network data and contributing to the team's research efforts.
### Response:
import networkx as nx
from cdlib import algorithms, evaluation

# Load a graph of coauthorships in network science
G = nx.read_gml('netscience.gml')

# Perform community detection using the Louvain method
louvain_communities = algorithms.louvain(G)

# Perform community detection using the Leiden method
leiden_communities = algorithms.leiden(G)

# Calculate the overlapping normalized mutual information
onmi_mgh = evaluation.overlapping_normalized_mutual_information_MGH(louvain_communities, leiden_communities)

print(onmi_mgh.score)
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