<｜begin▁of▁sentence｜>### Instruction:
A social network analyst is studying the community structure of a karate club network where nodes represent club members and edges represent their interactions. They are using the hierarchical leiden algorithm to detect communities that are well-connected internally but less connected to other groups. This is done to identify the varying levels of cliques and sub-cliques within the club. Due to the varying sizes of these groups, they utilize the hierarchical variant to recursively partition larger communities that exceed the defined maximum cluster size into smaller, more cohesive communities. How does the hierarchical leiden algorithm provide this multi-level community detection, and what is the final community assignment for each club member?
### Response:
import networkx as nx
from graspologic.partition import hierarchical_leiden

# Construct an example graph
G = nx.karate_club_graph()

# Run the hierarchical leiden algorithm on the graph
clusters = hierarchical_leiden(G, max_cluster_size=10, resolution=1.0, randomness=0.001, random_seed=42)

# Output the cluster assignment for each node at the final level
for cluster in clusters:
    if cluster.is_final_cluster:
        print(f'Node: {cluster.node_id}, Community: {cluster.cluster})
<|EOT|>
