<｜begin▁of▁sentence｜>### Instruction:
As a healthcare professional, imagine you have a large group of 100 patients, and you want to categorize them based on their health conditions and habits. Using a healthcare network analysis tool like NetworkX, with a probability of 0.1 for any two patients to share similar health conditions or habits, how can you create a patient clustering representation? And also, how could you calculate the overall health pattern (modularity) in the group, as well as determine the number of distinct health clusters present?
### Response:
from igraph import Graph, VertexClustering

# Suppose we have a graph object 'g'
g = Graph.Erdos_Renyi(n=100, p=0.1)

# Assume we have membership information for clustering
membership = [0] * 50 + [1] * 50

# Create a VertexClustering object with the graph and membership
vc = VertexClustering(g, membership=membership)

# To get the modularity of this clustering
modularity = vc.modularity
print('Modularity:', modularity)

# To get the number of clusters
num_clusters = len(vc)
print('Number of clusters:', num_clusters)
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