<｜begin▁of▁sentence｜>### Instruction:
In studying the social interactions within a karate club, a network graph is constructed where nodes represent members and edges represent social ties. The club is thought to have factions within it. Given a partitioning of the members into communities representing these factions, how can one quantify the strength or quality of the division? A modularity score can be computed to assess this aspect. Using an algorithm for modularity measurement, evaluate the proposed community assignments for the karate club members.
### Response:
import networkx as nx
from graspologic.partition import modularity

# Create a simple undirected graph
G = nx.karate_club_graph()

# Assume we have a community partition dictionary
communities = {0: 0, 1: 0, 2: 1, 3: 1, 4: 0, 5: 1, 6: 1, 7: 0, 8: 0, 9: 1, 10: 0}

# Calculate modularity
modularity_score = modularity(G, communities)
print(f'Modularity score: {modularity_score}')
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