<｜begin▁of▁sentence｜>### Instruction:
As a cartographer, you may be asked to create a map of a large city to help urban planners better understand the community structure. Let's consider a hypothetical scenario where you are tasked with analyzing a network of 100 neighborhoods within the city. Using the Erdos-Renyi model with a 5% probability of connections between neighborhoods, you're interested in determining the optimal number of distinct community clusters using the leading eigenvector community detection method. This information will be critical in visualizing and understanding the geographic distribution and interactions between different neighborhoods in the city.
### Response:
from igraph import Graph
import json

g = Graph.Erdos_Renyi(n=100, p=0.05)
vd = g.community_leading_eigenvector()
optimal_count = vd.optimal_count

print(json.dumps({'optimal_count': optimal_count}))
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