<｜begin▁of▁sentence｜>### Instruction:
In the world of social network analytics, researchers are often tasked with uncovering the dynamics of interconnected communities. Today, we turn our attention to an intriguing puzzle that mirrors the intricate dance of relationships within a social circle. Imagine a circle of friends represented by the following connections: pairs ('A', 'B'), ('B', 'C'), ('C', 'D'), ('D', 'E'), ('E', 'F'), and ('F', 'A'). The challenge facing our social analysts is to discover a pattern of relationships where no two pairs have a friend in common, yet any relationship not included in this pattern must share a friend with at least one pair that is included. The ultimate goal is to determine the smallest possible number of such unique pairs that accomplish this criterion. This scenario captivates those delving into the complexities of social structures, seeking to minimize overlaps while ensuring comprehensive coverage—a concept known as finding the minimum maximal matching in the technical parlance of graph theory. Stay tuned as we explore the data-driven insights that can unravel the fabric of social connectivity.
### Response:
import networkx as nx

# Create a graph
G = nx.Graph()
G.add_edges_from([('A', 'B'), ('B', 'C'), ('C', 'D'), ('D', 'E'), ('E', 'F'), ('F', 'A')])

# Find minimum maximal matching
matching = nx.algorithms.matching.min_maximal_matching(G)
print("Minimum maximal matching:", matching)
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