<｜begin▁of▁sentence｜>### Instruction:
As an SEO Specialist, let's consider a scenario where you're tasked with optimizing the visibility of a website that hosts a social network. Imagine this network represents connections between users, where each edge signifies a relationship between two users. Now, you're asked to utilize the `community_edge_betweenness` function available in the `igraph` library to analyze the community structure of this social network.

To achieve this, you'll need to extract the edge set data from the website's database, which includes connections between users. Then, you'll use the `community_edge_betweenness` function to identify clusters within the network based on the strength of connections between users. Finally, you'll print out the cluster membership for each user, providing insights into how users are grouped based on their relationships within the network.

This analysis will help in understanding the natural divisions or communities within the user base of the website, allowing for targeted optimization strategies to enhance user engagement and overall website performance in search engine rankings.
### Response:
from igraph import Graph

# Create a Graph
edges = [(0, 1), (0, 2), (1, 2), (1, 3), (2, 3), (3, 4), (4, 5), (4, 6), (5, 6)]
g = Graph(edges=edges, directed=False)

# Calculate community structure based on edge betweenness
dendrogram = g.community_edge_betweenness()
# Convert dendrogram into actual clustering
clusters = dendrogram.as_clustering()

# Print the membership list for each vertex
class_membership = clusters.membership
print(class_membership)
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