<｜begin▁of▁sentence｜>### Instruction:
As a Network Security Engineer tasked with safeguarding the structural integrity of our virtual communication dojo, suppose we have a network representing the interactions within a Karate Club. We'd like to get a strategic view of the network's hierarchy by implementing a form of compartmentalization or clustering. Specifically, we're curious about the initial state of our network's clusters and the ultimate configuration when the clustering is fully applied.

To achieve this, we'd utilize a method known as Hierarchical Clustering - a technique commonly found in the NetworkX toolkit, which helps us understand the multilevel modular structure of our network. Could you deploy a HierarchicalCluster algorithm to deduce the communal factions at both the inception stage and the terminal consolidation of our network? Your mission is to extract and report the cluster composition at these two critical junctures. Remember, maintaining the semantics of our network's relationships in your analysis is paramount for a clear interpretation of the clustering results.
### Response:
import networkx as nx
from graspologic.partition import HierarchicalCluster, hierarchical_leiden

# Create a graph
g = nx.karate_club_graph()

# Perform hierarchical leiden clustering
hierarchical_clusters = hierarchical_leiden(g)

# Access the first and final level of hierarchical clustering
initial_clustering = hierarchical_clusters.first_level_hierarchical_clustering()
final_clustering = hierarchical_clusters.final_level_hierarchical_clustering()

# Print initial and final hierarchical clustering
print('Initial clustering:', initial_clustering)
print('Final clustering:', final_clustering)
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