The part that tripped me up was the same-timestamp multi-hop behavior.
Sort the meetings by timestamp, then process each timestamp group together: for each group, first collect all people who know the secret before any meetings at that time, then propagate the secret through all meetings in that group simultaneously. This ensures that within the same timestamp, the secret can spread across multiple meetings in a chain. Finally, return the set of people who know the secret.
Pro tip: Emphasize that processing each timestamp as a batch is crucial to handle chained propagation correctly; a common mistake is to process meetings sequentially, which fails when meetings at the same time are interdependent.
Clarify that meetings are given as (time, person1, person2) and that within the same timestamp, the secret can spread through a chain of meetings. Confirm that person 0 and one other person initially know the secret.
Sort the list of meetings in ascending order of time. This groups meetings that occur at the same timestamp together.
For each group of meetings with the same timestamp, first determine which people know the secret before any meetings at that time. Then, simulate the spread: for each meeting, if either person knows the secret, the other learns it. Because meetings are simultaneous, repeat this propagation until no new person learns the secret within the group (or use a union-find approach).
After processing all meetings in the group, update the global set of people who know the secret to include those who learned it during this timestamp.
After all timestamp groups are processed, return the set of people who know the secret.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.