<｜begin▁of▁sentence｜>### Instruction:
As a network analyst, I can help you evaluate how well a specific community assignment reflects the actual connections between individuals in a social network. For instance, imagine you're working with a social media platform and you want to assess the effectiveness of the community detection algorithm in identifying natural groups of users based on their interactions. By using the modularity function in networkx, you can quantify the extent to which the detected communities capture the underlying structure of the network, considering the density of connections within and between the identified groups. This can provide valuable insights into the accuracy of the community assignment and its alignment with the real social dynamics within the platform.
### Response:
from igraph import Graph

# Create a graph from an edge list
edges = [(0, 1), (0, 2), (1, 2), (3, 4), (4, 5), (3, 5)]
g = Graph(edges=edges, directed=False)

# Define a clustering (community assignment)
# In this example, vertices 0-2 are in one community, and 3-5 are in another
membership = [0, 0, 0, 1, 1, 1]

# Calculate the modularity of the graph with respect to the clustering
modularity_score = g.modularity(membership)

# Print the modularity score
print(modularity_score)
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