<｜begin▁of▁sentence｜>### Instruction:
Imagine we're looking at the health network of a small community with 30 individuals, where the likelihood of any two individuals having a connection (akin to an interaction or a relationship) is 5%. This network is like a representation of who might share health advice or influence with whom. Now, the community is naturally splitting into three groups where individuals 0 to 5 share a strong bond, as do individuals 6 to 10. The rest of the individuals form another distinctive group.

If we were to analyze this community's network to understand how well these groups stand in terms of internal interaction versus external interaction (similar to how we evaluate the effectiveness of a treatment plan within distinct patient demographics), I need to calculate two measures of this community division: "coverage" - to gauge the proportion of interactions within the groups compared to the total network, and "performance" – which would tell us how many interactions are taking place strictly within these groups versus across different groups.

Could we apply data analysis tools to compute these metrics, which are like an indicator of the community's health in terms of social cohesion? This would help us in mirroring these concepts back to our healthcare domain, providing insights similar to assessing the impact of a preventive healthcare strategy within segmented patient groups.
### Response:
import networkx as nx
from networkx.algorithms.community.quality import partition_quality

# Create a graph
G = nx.erdos_renyi_graph(30, 0.05)

# Assuming we have a predefined partition of the graph
partition = [{0, 1, 2, 3, 4, 5}, {6, 7, 8, 9, 10}, {11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29}]

# Calculate the coverage and performance
coverage, performance = partition_quality(G, partition)

print('Coverage:', coverage)
print('Performance:', performance)
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