<｜begin▁of▁sentence｜>### Instruction:
As a Fashion Blogger creating a post about the latest fashion trends and styles, imagine you have access to a network of fashion influencers. In this network, you have information about collaborations between influencers represented as connections between them. For example, Influencer A collaborates with Influencer B, Influencer C collaborates with Influencer A, and so on.

If you were to analyze this network using the `VertexClustering.sizes` function in the `igraph` API for community detection, you could find out the sizes of the clusters formed based on these collaboration patterns. This would give you insights into the groupings of influencers within the fashion community based on their collaborative efforts.
### Response:
from igraph import Graph
import igraph as ig

g = ig.Graph(edges=[(0, 1), (0, 2), (1, 2), (3, 4), (4, 5), (3, 5)])
# Perform community detection to cluster the graph
clustering = g.community_edge_betweenness()
# Convert it to VertexClustering
vertex_clustering = ig.VertexClustering(g, clustering.membership)
# Get the sizes of the clusters
cluster_sizes = vertex_clustering.sizes()
print(cluster_sizes)
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