I got the basic loop right pretty quickly: iterate over U's followees, then over each of their followers, accumulate counts in a hashmap, then use a min-heap to pull out top K.
Clarify the graph representation and constraints, then propose a two-phase algorithm: first gather all followees of U, then for each followee, iterate over their followees and count occurrences, excluding U and already-followed users. Finally, select the top K candidates using a min-heap or sorting, and analyze time and space complexity.
Pro tip: Mention that in a real system, this recommendation is often precomputed offline or approximated with MapReduce/streaming due to scale, and discuss trade-offs between exactness and latency.
Ask about graph size, whether the graph is directed (follow is directed), if U can be recommended to themselves, and if candidates already followed by U should be excluded.
Represent the graph as an adjacency list for followees. Use a hash map to count how many of U's followees follow each candidate, iterating over each followee's followees.
Exclude U and users already followed by U from recommendations. Handle cases where U has no followees, no candidates, or K is larger than the number of candidates.
Use a min-heap of size K to track top candidates in O(N log K) time, or sort all candidates in O(N log N) if K is large. Discuss trade-offs.
Time complexity: O(F * A + N log K) where F is number of followees, A is average followees per followee, N is number of candidates. Space: O(N). Mention distributed approaches for large graphs.
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