<｜begin▁of▁sentence｜>### Instruction:
Given an undirected social network graph, such as the Karate Club graph often used in network science studies, how can we use the Leiden algorithm to detect the community structure of the graph and obtain a partitioning where nodes are grouped into communities in a way that maximizes a chosen quality function, such as modularity?
### Response:
import networkx as nx
from graspologic.partition import leiden

# Step 1: Construct the social network graph
G = nx.karate_club_graph()

# Step 2: Run the Leiden algorithm to find the best partitioning
partitions = leiden(G)

# Step 3: Print the resulting community partitioning
top1_community = partitions[1]
print(f'Node 1 is in community {top1_community}')
print(partitions)
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