<｜begin▁of▁sentence｜>### Instruction:
In conducting an analysis of a simulated economic network comprising 10 distinct entities—akin to firms or regions—generated through the Erdős-Rényi framework with a pairing likelihood of 50%, we discover that the network has naturally segregated into three clusters, analogous to economic sectors or trade groups, delineated as follows: Sector A includes entities 0, 1, 2, and 3; Sector B is composed of entities 4, 5, and 6; and Sector C encompasses entities 7, 8, and 9. 

For this illustrative economic model contained within our simulation, could you elucidate the methodology by which NetworkX could be harnessed to calculate the degree of internal interactions (coverage) and the effectiveness of the divisions (performance) within this partitioned network structure? These metrics will be instrumental in gauging how well the sectors internalize commerce versus their interactions with other sectors, thereby providing insight into the robustness and efficiency of this economic division.
### Response:
import networkx as nx
from networkx.algorithms.community.quality import partition_quality

# Create a graph
G = nx.erdos_renyi_graph(10, 0.5)

# Define a partition of the graph into communities
partition = [{0, 1, 2, 3}, {4, 5, 6}, {7, 8, 9}]

# Calculate the quality of the partition
cov, perf = partition_quality(G, partition)

print('Coverage:', cov)
print('Performance:', perf)
<|EOT|>
