<｜begin▁of▁sentence｜>### Instruction:
As a patent attorney, you may need to analyze the clustering structure of a network representing a group of patents or inventions. For instance, you might have a network representing the relationships between different patents based on their citations or shared technologies. Upon performing community detection, you could use the cluster_graph function in the networkx library to contract each cluster into a single vertex, creating a new graph that depicts the clustering structure. This can help you better understand the relationships and patterns within different groups of patents, ultimately assisting in safeguarding intellectual assets and fostering innovation.
### Response:
from igraph import Graph

# Define the edges of the graph g
edges = [(0, 1), (1, 2), (0, 2), (3, 4), (4, 5), (5, 3)]
g = Graph(edges=edges)

# Perform clustering (community detection) on the graph
# For simplicity, let's assume we get the following clusters:
clusters = g.community_edge_betweenness().as_clustering()

# Now, we can create the cluster graph
cluster_graph = clusters.cluster_graph()

# Print the summary of the cluster graph to observe the contracted vertices
print(cluster_graph.summary())
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