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I went with an adjacency list using a hash map of sets, which felt right for this kind of undirected relationship problem.
Start by clarifying requirements and constraints, then propose an adjacency list using a hash map (or dictionary) of sets to store connections, which supports O(1) average-time operations. Write clean, modular code for connect, disconnect, and clear, and analyze time and space complexity for each operation.
Pro tip: Mention that using a set for each user's connections ensures no duplicate connections and makes disconnect O(1) on average; also note that clearing all connections can be O(1) if you simply reassign the main map to a new empty map, but be prepared to discuss the trade-off with garbage collection.
Ask whether connections are bidirectional, if self-connections are allowed, and if there are any limits on the number of users or connections. Confirm that operations should be as efficient as possible.
Propose an adjacency list using a hash map where each key is a user ID and the value is a set of connected user IDs. Explain why a set is better than a list for O(1) average-time add, remove, and lookup.
Write code for connect(user1, user2), disconnect(user1, user2), and clearAll(). Ensure connect adds each user to the other's set (if bidirectional), disconnect removes them, and clearAll resets the map.
For each operation, state the average and worst-case time complexity and the space complexity. For example, connect and disconnect are O(1) average, clearAll is O(1) if reassigning the map, and space is O(U + C) where U is users and C is connections.
Mention alternative data structures (e.g., adjacency matrix) and their trade-offs, and suggest possible extensions like thread safety or persistence if relevant.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.